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1. Going Global: Scaling the Artificial Intelligence Literacy Course to an International Audience

Author: Andrew Dennis Smith, Hari Trivedi, Houman Sotoudeh, Jordan D Perchik, Marcelo Straus Takahashi, Paul H Yi, Srini Tridandapani, Steven A Rothenberg, and Vishwa Parekh
Subjects: Artificial Intelligence, Machine learning, Remote education, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Introduction: Applications of artificial intelligence (AI) in radiology continue to increase every year, however most radiology residencies lack a dedicated AI education curriculum. Fundamental AI education resources are even more sparse for trainees in low- to middle-income countries and under-resourced healthcare systems. The AI Literacy Course assesses the effectiveness and scalability of a free, remote AI education curriculum to increase understanding of fundamental AI terms, methods, and applications in radiology among radiology trainees in the United States and internationally. Method: A week-long AI in radiology literacy course for radiology trainees was held October 3-7, 2022. Ten 30-minute lectures utilizing a remote learning format covered basic AI terms and methods, clinical applications of AI in radiology by three different subspecialties, and special topics lectures. A proctored, hands-on clinical AI session allowed participants to directly use an FDA-cleared, AI-assisted viewer and reporting system for advanced cancer. Pre- and post-course electronic surveys were distributed to assess participants’ knowledge of AI terminology and applications, as well as their interest in AI education. Results: A total of 25 residency programs throughout the US participated in the course with participants attending from 10 countries. An average of 150 participants viewed the course per day. Nearly all participants reported insufficient exposure to AI in their radiology training (95.8%). Participant knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course evaluation of 8.3/15 and a post-course evaluation of 10.0/15 (p=0.01). Conclusion: The scalability of the AI Literacy Course demonstrates a viable model to bring accessible fundamental AI education to radiology trainees in the United States and internationally.
Published: 2024
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2. Imaging spectrum of amyloid-related imaging abnormalities associated with aducanumab immunotherapy

Author: Houman Sotoudeh, Mohammadreza Alizadeh, Ramin Shahidi, Parnian Shobeiri, Zahra Saadatpour, C. Austin Wheeler, Marissa Natelson Love, and Manoj Tanwar
Subjects: Alzheimer’s disease, amyloid related imaging abnormalities, aducanumab, neuroimaging, anti amyloid aggregation, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Alzheimer's Disease (AD) is a leading cause of morbidity. Management of AD has traditionally been aimed at symptom relief rather than disease modification. Recently, AD research has begun to shift focus towards disease-modifying therapies that can alter the progression of AD. In this context, a class of immunotherapy agents known as monoclonal antibodies target diverse cerebral amyloid-beta (Aβ) epitopes to inhibit disease progression. Aducanumab was authorized by the US Food and Drug Administration (FDA) to treat AD on June 7, 2021. Aducanumab has shown promising clinical and biomarker efficacy but is associated with amyloid-related imaging abnormalities (ARIA). Neuroradiologists play a critical role in diagnosing ARIA, necessitating familiarity with this condition. This pictorial review will appraise the radiologic presentation of ARIA in patients on aducanumab.
Published: 2024
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3. Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform

Author: Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine, and Houman Sotoudeh
Subjects: glioblastoma multiforme, AI-based segmentation, Sørensen–Dice score, neuro-radiology, MRI imaging, sensitivity and specificity, Medicine (General), R5-920
Abstract: Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists’ ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists’ scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists’ scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (
Published: 2024
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4. ALK-positive histiocytosis involving the cavernous sinus: A deceptive radiologic mimic of meningioma

Author: Mohammadreza Alizadeh, MD, Aishwarya Ravindran, MD, Rati Chkheidze, MD, Gaurav Goyal, MD, Mahdie Hosseini, MD, Parnian Shobeiri, MD, Arezoo Shafieioun, MD, Mohammad Khalafi, MD, Ramin Shahidi, Houman Sotoudeh, MD, and Aparna Singhal, MD
Subjects: ALK-positive histiocytosis, Histiocytosis, Cavernous sinus, Magnetic resonance imaging, Meningioma, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Anaplastic lymphoma kinase (ALK)-positive histiocytosis is an uncommon condition, recently considered a separate condition from other histiocytosis by WHO 5th edition. It can involve intracranial structures. This manuscript describes a case of ALK-positive histiocytosis of the cavernous sinus, focusing on the radiologic and pathologic presentation of the entity. Our case had MRI manifestations mimicking meningioma, metastasis, and Langerhans histiocytosis. On CT imaging, benign osseous remodeling of the cavernous sinus was detected, which can be helpful in differentiating it from more common meningioma.
Published: 2023
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5. Magnetic Resonance Imaging (MRI) Findings in COVID-19 Associated Encephalitis

Author: Manoj Tanwar, Aparna Singhal, Mohammadreza Alizadeh, and Houman Sotoudeh
Subjects: COVID-19, magnetic resonance imaging, encephalitis, Medicine, Internal medicine, RC31-1245, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: We conducted this study to investigate the scope of the MRI neuroimaging manifestations in COVID-19-associated encephalitis. From January 2020 to September 2021, patients with clinical diagnosis of COVID-19-associated encephalitis, as well as concomitant abnormal imaging findings on brain MRI, were included. Two board-certified neuro-radiologists reviewed these selected brain MR images, and further discerned the abnormal imaging findings. 39 patients with the clinical diagnosis of encephalitis as well as abnormal MRI findings were included. Most (87%) of these patients were managed in ICU, and 79% had to be intubated-ventilated. 15 (38%) patients died from the disease, while the rest were discharged from the hospital. On MRI, FLAIR hyperintensities in the insular cortex were the most common finding, seen in 38% of the patients. Micro-hemorrhages on the SWI images were equally common, also seen in 38% patients. FLAIR hyperintensities in the medial temporal lobes were seen in 30%, while FLAIR hyperintensities in the posterior fossa were evident in 20%. FLAIR hyperintensities in basal ganglia and thalami were seen in 15%. Confluent FLAIR hyperintensities in deep and periventricular white matter, not explained by microvascular angiopathy, were detected in 7% of cases. Cortical-based FLAIR hyperintensities in 7%, and FLAIR hyperintensity in the splenium of the corpus callosum in 7% of patients. Finally, isolated FLAIR hyperintensity around the third ventricle was noted in 2% of patients.
Published: 2023
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6. Subcortical signal alteration of corticospinal tracts. A radiologic manifestation of ARIA: A case report

Author: Houman Sotoudeh, MD, Mohammadreza Alizadeh, MD, Ramin Shahidi, MD, Parnian Shobeiri, MD, MPH, Natelson love, MD, and Aparna Singhal, MD
Subjects: Alzheimer Disease, Amyloid-related imaging abnormalities, Magnetic Resonance Imaging, aducanumab, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Patients with Alzheimer's disease who have been given monoclonal antibodies targeting amyloid-β (Aβ) (eg, gantenerumab, donanemab, lecanemab, and aducanumab) for scientific purposes may have a spectrum of imaging findings known as amyloid-related imaging abnormalities (ARIA), shown on brain magnetic resonance imaging (MRI) scans. These neuroimaging abnormalities are caused by antibody-mediated destruction of accumulated Aβ aggregates in cerebral blood vessels and brain parenchyma. ARIA may demonstrate as brain edema or sulcal effusion (ARIA-E) or as hemosiderin deposits caused by brain parenchymal or pial hemorrhage (ARIA-H). The current study explores 2 cases with interval development of FLAIR hyper signal intensity along the bilateral corticospinal tracts in the motor cortex/precentral gyri after treatment by aducanumab. We believe this manifestation is a subtype of ARIA-A that has not been explored earlier. Our first case was a 72-year-old woman with a history of HTN and kidney transplant (polycystic kidney) who presented with mild cognitive impairment with clinical findings consistent with early Alzheimer's disease. After receiving 3 doses of aducunumab and experiencing cognition improvement, she underwent a brain MRI because of dizziness and vertigo. The brain MRI demonstrated new FLAIR hyper signal intensity in subcortical regions of precentral gyri (motor cortex) symmetrically as well as trace subarachnoid hemorrhage at the vertex compatible with ARIA-E and ARIA-H. Our second case was an 85-year-old woman with a history of small lymphocytic leukemia which was treated 20 years earlier. After orthopedic surgery 2 years ago, she developed dementia with anterograde amnesia. Since then, Aricept and Namenda have been started, but there have been no improvements in her subjective condition. The initial Amyloid PET/MR imaging showed diffuse cerebral Amyloid deposition. After tolerating 6 doses of aducanumab a safety MRI revealed new bilateral symmetric FLAIR hyper signal intensity in the subcortical motor cortex. Results of our study suggest that the subcortical corticospinal tract is another hotspot for ARIA findings. Hence, these regions might be an unknown site for both the action and adverse effects of aducanumab on amyloid plaques with secondary inflammation. In addition, radiologists must take this phenomenon into the account, and be cognizant that the FLAIR hyper signal intensities should not be misinterpreted as motor neuron disease (eg, amyotrophic lateral sclerosis).
Published: 2023
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7. Prediction of in-hospital adverse clinical outcomes in patients with pulmonary thromboembolism, machine learning based models

Author: Yaser Jenab, Kaveh Hosseini, Zahra Esmaeili, Saeed Tofighi, Hamid Ariannejad, and Houman Sotoudeh
Subjects: machine learing, outcome analysis, risk factors, logistic models, gradient boosting machine, pulmonary embolism, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: BackgroundPulmonary thromboembolism (PE) is the third leading cause of cardiovascular events. The conventional modeling methods and severity risk scores lack multiple laboratories, paraclinical and imaging data. Data science and machine learning (ML) based prediction models may help better predict outcomes.Materials and methodsIn this retrospective registry-based design, all consecutive hospitalized patients diagnosed with pulmonary thromboembolism (based on pulmonary CT angiography) from 2011 to 2019 were recruited. ML based algorithms [Gradient Boosting (GB) and Deep Learning (DL)] were applied and compared with logistic regression (LR) to predict hemodynamic instability and/or all-cause mortality.ResultsA total number of 1,017 patients were finally enrolled in the study, including 465 women and 552 men. Overall incidence of study main endpoint was 9.6%, (7.2% in men and 12.4% in women; p-value = 0.05). The overall performance of the GB model is better than the other two models (AUC: 0.94 for GB vs. 0.88 and 0.90 for DL and LR models respectively). Based on GB model, lower O2 saturation and right ventricle dilation and dysfunction were among the strongest adverse event predictors.ConclusionML-based models have notable prediction ability in PE patients. These algorithms may help physicians to detect high-risk patients earlier and take appropriate preventive measures.
Published: 2023
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8. Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme

Author: Mohammadreza Alizadeh, Nima Broomand Lomer, Mobin Azami, Mohammad Khalafi, Parnian Shobeiri, Melika Arab Bafrani, and Houman Sotoudeh
Subjects: glioma, glioblastoma multiform (GBM), radiomics, MRI, PET, tumor progression, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics’s capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.
Published: 2023
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9. Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept

Author: Mohsen Tabatabaei, Baharak Tasorian, Manu Goyal, Abdollatif Moini, and Houman Sotoudeh
Subjects: covid-19 ● influenza, human ● artificial intelligence ● tomography, Medicine (General), R5-920
Abstract: Background: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task.Methods: In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest.Results: The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%).Conclusion: In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.
Published: 2021
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10. Evaluation of RANO Criteria for the Assessment of Tumor Progression for Lower-Grade Gliomas

Author: Fabio Raman, Alexander Mullen, Matthew Byrd, Sejong Bae, Jinsuh Kim, Houman Sotoudeh, Fanny E. Morón, and Hassan M. Fathallah-Shaykh
Subjects: Response Assessment in Neuro-Oncology (RANO) criteria, statistical change-of-point method, low-grade gliomas, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Purpose: The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator’s discretion of the perpendicular diameter of the largest tumor cross-section. Potential sources of error include acquisition inconsistency of 2D slices, operator selection variabilities in both representative tumor cross-section and measurement line locations, and the inability to quantify infiltrative tumor margins and satellite lesions. Our goal was to assess the accuracy and reproducibility of RANO in LG. Materials and Methods: A total of 651 FLAIR MRIs from 63 participants with LGGs were retrospectively analyzed by three blinded attending physicians and three blinded resident trainees using RANO criteria, 2D visual assessment, and computer-assisted 3D volumetric assessment. Results: RANO product measurements had poor-to-moderate inter-operator reproducibility (r2 = 0.28–0.82; coefficient of variance (CV) = 44–110%; mean percent difference (diff) = 0.4–46.8%) and moderate-to-excellent intra-operator reproducibility (r2 = 0.71–0.88; CV = 31–58%; diff = 0.3–23.9%). When compared to 2D visual ground truth, the accuracy of RANO compared to previous and baseline scans was 66.7% and 65.1%, with an area under the ROC curve (AUC) of 0.67 and 0.66, respectively. When comparing to volumetric ground truth, the accuracy of RANO compared to previous and baseline scans was 21.0% and 56.5%, with an AUC of 0.39 and 0.55, respectively. The median time delay at diagnosis was greater for false negative cases than for false positive cases for the RANO assessment compared to previous (2.05 > 0.50 years, p = 0.003) and baseline scans (1.08 > 0.50 years, p = 0.02). Conclusion: RANO-based assessment of LGGs has moderate reproducibility and poor accuracy when compared to either visual or volumetric ground truths.
Published: 2023
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11. A solitary hypothalamic metastasis from prostatic cancer mimicking a giant thrombotic aneurysm and presenting with intraventricular hemorrhage and acute hydrocephalus: a case report

Author: Zarhra Saadatpour, Ali Rezaei, Aparna Singhal, Houman Sotoudeh, and Kamran Tavakol
Subjects: Prostatic neoplasm, Cerebral intraventricular hemorrhage, Neoplastic metastasis, Aneurysm, Hydrocephalus, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Abstract Background Despite the high prevalence of prostate cancer, its brain parenchymal metastasis is not common and intracranial hemorrhage due to such a metastasis is even less common. This report presents a challenging case of solitary brain metastasis secondary to prostate cancer that gave rise to intraventricular hemorrhage and acute hydrocephalus mimicking a giant aneurysm. Case presentation A 77-year-old man with a history of prostate cancer, hypertension, and morbid obesity presented to the emergency room with a severe headache. He was afebrile with a blood pressure of 144/79 mmHg, alert, without any sign of sensory or motor deficit. Shortly after admission, he became unresponsive and was immediately intubated. His blood tests revealed hypernatremia at 154 mmol/L; otherwise, the lab data including the COVID-19 screening proved normal. The cerebral CT and MR images, with and without contrast, were interpreted as a giant thrombotic aneurysm extending to the suprasellar region by the emergency radiologist. Also, moderate intraventricular hemorrhage, acute hydrocephalus, and sub-ependymal interstitial edema were observed. Upon further evaluation of the images, the lesion was determined to be an exophytic hemorrhagic hypothalamic mass, and the subsequent biopsy was consistent with prostate cancer metastasis. Conclusions The exophytic hemorrhagic hypothalamic metastasis can mimic a ruptured aneurysm on imaging. Given the improved survival of patients with prostate cancer, radiologists may encounter such unusual cerebral metastases from prostate cancers more frequently in the future.
Published: 2020
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12. Intraventricular metastatic melanoma: A case report and review of the literature

Author: Joshua D. Bernstock, Gustavo Chagoya, Galal A. Elsayed, Brandon M. Fox, Nabiel Mir, Saksham Gupta, Melissa Chua, Travis J. Atchley, Mina Lobbous, Houman Sotoudeh, James Hackney, Gregory K. Friedman, and Mark R. Harrigan
Subjects: central nervous system metastasis, intraventricular metastasis, melanoma, metastatic melanoma, Medicine, Medicine (General), R5-920
Abstract: Abstract Intraventricular melanoma is a very rare and highly malignant disease. Safe resection is the mainstay of treatment, but no standard guidelines exist for adjuvant therapy. Early histologic and molecular diagnosis is key for improved survival.
Published: 2020
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13. Cauda equina syndrome in an ovarian malignant‐mixed müllerian tumor with leptomeningeal spread

Author: Joshua D. Bernstock, Stuart Ostby, Brandon Fox, Houman Sotoudeh, Andrew Janssen, Yun Jee Kang, Jason Chen, Veeranjaneyulu Prattipati, Galal Elsayed, Gustavo Chagoya, Daisuke Yamashita, Gregory K. Friedman, Burt Nabors, Warner K. Huh, and Mina Lobbous
Subjects: cauda equina syndrome, leptomeningeal carcinomatosis, metastasis, ovarian carcinosarcoma, ovarian malignant‐mixed müllerian tumor, Medicine, Medicine (General), R5-920
Abstract: Abstract Leptomeningeal metastasis is extremely rare in patients with ovarian cancer, but should be considered in patients presenting with neurologic deficits such as cauda equine syndrome. Given its poor prognosis and lack of data currently on management, additional studies are needed to optimize treatment regimens and improve outcomes.
Published: 2019
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14. Luxury perfusion: A paradoxical finding and pitfall of CT perfusion in subacute infarction of brain

Author: Houman Sotoudeh, Assistant Professor, Omid Shafaat, Research Fellow, Aparna Singhal, Assistant Professor, and Asim Bag, Associated Professor
Subjects: Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: In the context of recent guidelines for stroke management, application of computed tomographic perfusion (CTP) is anticipated to increase, especially within 6-24 hours after stroke onset. There are commercially available software packages to help to detect the infarct core and the penumbra, however, the results of these software are not always straightforward that might cause incorrect CTP interpretation. We present here a case of acute ischemic stroke in which the CTP software misinterpreted the results by labeling the normal hemisphere as infarction and ischemia due to luxury perfusion of the infarcted hemisphere. Awareness of laterality of the patient's symptoms and the understanding of functioning of perfusion analysis software is necessary to avoid this pitfall. Keywords: Perfusion imaging, Stroke, Neurologic examination
Published: 2019
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15. Superior ophthalmic vein thrombosis: What radiologist and clinician must know?

Author: Houman Sotoudeh, Omid Shafaat, Noha Aboueldahab, Michael Vaphiades, Ehsan Sotoudeh, and Joshua Bernstock
Subjects: Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Purpose: Superior ophthalmic vein thrombosis (SOVT) is an extremely rare condition. Few studies have been published about clinical aspects of this condition. In this study, we have studied the symptoms, underlying etiologies, treatment, pathogenesis and complication of the SOVT and we tried to classify it based on the etiology, treatment, and prognosis. Methods: We reviewed the patients’ data from a tertiary academic referral center. Each patient with SOVT was then reviewed for symptoms associated with SOVT, underlying etiology, treatment protocol, treatment response, complications, possible pathogens, and final outcome. Results: Twenty-four cases of SOVT were included in this study. Overall, 13 cases were diagnosed as right-sided SOVT, out of which, eight had simultaneous right-sided cavernous sinus thrombosis (CST). Eighteen cases were diagnosed to have left-sided SOVT, out of which, 11 had simultaneous left-sided CST. Conclusions: The SOVT can be secondary to different mechanisms. The SOVT secondary to trauma, recent surgery and coagulopathy are mostly non-aggressive, and can be managed by conservative therapy and anticoagulation. The SOVT in patients with orbital cellulitis, history of active sinusitis or paranasal sinus surgery are aggressive presenting with acute orbital swelling, abscess and visual loss. This type of SOVT can be complicated by extension to the cavernous sinus and intracranial structures. These patients require urgent antibiotics therapy and sinus surgery. The most severe type of SOVT is caused by mucormycosis which may also extend intracranially resulting in stroke and is often life-threatening. Keywords: Superior ophthalmic vein thrombosis, Thrombophlebitis, Cavernous sinus thrombosis, Orbital cellulitis, Sinusitis
Published: 2019
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16. Spinal canal meningioma mimicking posterior fossa ischemia on CT perfusion: A CT perfusion pitfall

Author: Houman Sotoudeh, MD
Subjects: Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: The recent approach to treat acute stroke is to extend treatment window in patients with salvageable peri-infarct ischemia which increases the application of the perfusion imaging, specifically computed tomography perfusion (CTP). In this paper, I am presenting a case of left middle cerebral artery infarction which was evaluated by CTP under “code stroke.” The patient had an incidental spinal canal meningioma which was out of field of view in CTP but mimicked right cerebellar ischemia on CTP. Although ischemia has been previously reported within the peripheral parenchymal edema surrounding a meningioma, in this patient there was no evidence of edema in the right cerebellum on magnetic resonance imaging. I believe the CTP findings are secondary to steal phenomena at right vertebral artery or compression upon the venous plexus. Recently, by using modern computed tomography scanners, it is common to cover the entire brain in CTP. The emergency radiologist should be aware of this pitfall that spinal canal pathologies which are out of field of view can mimic posterior fossa ischemia. Keywords: Stroke, CT perfusion, Meningioma, Ischemia
Published: 2018
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17. Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine

Author: Houman Sotoudeh, Omid Shafaat, Joshua D. Bernstock, Michael David Brooks, Galal A. Elsayed, Jason A. Chen, Paul Szerip, Gustavo Chagoya, Florian Gessler, Ehsan Sotoudeh, Amir Shafaat, and Gregory K. Friedman
Subjects: glioma, artificial intelligence, neural network, deep neural network, convolution neural network, support vector machines, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy.Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma.Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis.Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.
Published: 2019
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18. SUMOylation in Glioblastoma: A Novel Therapeutic Target

Author: Brandon M. Fox, Andrew Janssen, Dagoberto Estevez-Ordonez, Florian Gessler, Nunzio Vicario, Gustavo Chagoya, Galal Elsayed, Houman Sotoudeh, William Stetler, Gregory K. Friedman, and Joshua D. Bernstock
Subjects: glioblastoma (GBM), SUMOylation, SUMO1-3, post-translational modifications (PTMs), Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract: Protein SUMOylation is a dynamic post-translational modification which is involved in a diverse set of physiologic processes throughout the cell. Of note, SUMOylation also plays a role in the pathobiology of a myriad of cancers, one of which is glioblastoma (GBM). Accordingly, herein, we review core aspects of SUMOylation as it relates to GBM and in so doing highlight putative methods/modalities capable of therapeutically engaging the pathway for treatment of this deadly neoplasm.
Published: 2019
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19. Radiomics for differentiation of the posterior fossa pilocytic astrocytoma versus hemangioblastomas in adults. A pilot study

Author: Houman Sotoudeh, Zahra Saadatpour, Ali Rezaei, Mahsan Sotoudeh, Charles A Wheeler, Aparna Singhal, and Manoj Tanwar
Subjects: Adult, Humans, Pilot Projects, Radiology, Nuclear Medicine and imaging, Astrocytoma, Magnetic Resonance Imaging, Hemangioblastoma, Retrospective Studies
Abstract: Purpose:Both pilocytic astrocytoma (PA) and hemangioblastoma (HB) are common primary neoplasms of the posterior fossa with similar radiological manifestations. This study was conducted to evaluate the role of radiomics in differentiating these two conditions in adults. Materials and Methods:After a retrospective search of our institutional imaging archive, adult patients with a known diagnosis of PA or HB were included. We reviewed each patient’s most recent preoperative brain magnetic resonance imaging (MRI). The solid enhancing nodule of each lesion on post-contrast T1 sequence was manually segmented. Multiple radiomic features were then extracted from each nodule using the Pyradiomic library. Subsequently, the most predictive features were identified by feature selection models. Following this, different machine learning (ML) models were constructed based on these selected features to classify lesions as PA or HB. Finally, we evaluated the performance of each model by leave-one-out cross-validation. Results:With inclusion and exclusion criteria, 34 enhancing PA nodules and 39 HB nodules were selected. A total of 115 features were extracted from each enhancing nodule. Twelve characteristics were detected as most predictive of histopathological diagnosis. Among various ML models, the neural network had the best performance in differentiating these two conditions with an AUC of 0.9 and an accuracy of 82%. Conclusions:In this retrospective study, radiomic MRI techniques demonstrated high performance in distinguishing adult posterior fossa PA from HB. Future development of radiomic models may advance presurgical diagnosis of these two conditions when added to routine clinical practice and thus improve patient management.
Published: 2023

20. Evaluation of RANO Criteria for the Assessment of Tumor Progression for Lower-Grade Gliomas

Author: Fathallah-Shaykh, Fabio Raman, Alexander Mullen, Matthew Byrd, Sejong Bae, Jinsuh Kim, Houman Sotoudeh, Fanny E. Morón, and Hassan M.
Subjects: Response Assessment in Neuro-Oncology (RANO) criteria, statistical change-of-point method, low-grade gliomas
Abstract: Purpose: The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator’s discretion of the perpendicular diameter of the largest tumor cross-section. Potential sources of error include acquisition inconsistency of 2D slices, operator selection variabilities in both representative tumor cross-section and measurement line locations, and the inability to quantify infiltrative tumor margins and satellite lesions. Our goal was to assess the accuracy and reproducibility of RANO in LG. Materials and Methods: A total of 651 FLAIR MRIs from 63 participants with LGGs were retrospectively analyzed by three blinded attending physicians and three blinded resident trainees using RANO criteria, 2D visual assessment, and computer-assisted 3D volumetric assessment. Results: RANO product measurements had poor-to-moderate inter-operator reproducibility (r2 = 0.28–0.82; coefficient of variance (CV) = 44–110%; mean percent difference (diff) = 0.4–46.8%) and moderate-to-excellent intra-operator reproducibility (r2 = 0.71–0.88; CV = 31–58%; diff = 0.3–23.9%). When compared to 2D visual ground truth, the accuracy of RANO compared to previous and baseline scans was 66.7% and 65.1%, with an area under the ROC curve (AUC) of 0.67 and 0.66, respectively. When comparing to volumetric ground truth, the accuracy of RANO compared to previous and baseline scans was 21.0% and 56.5%, with an AUC of 0.39 and 0.55, respectively. The median time delay at diagnosis was greater for false negative cases than for false positive cases for the RANO assessment compared to previous (2.05 > 0.50 years, p = 0.003) and baseline scans (1.08 > 0.50 years, p = 0.02). Conclusion: RANO-based assessment of LGGs has moderate reproducibility and poor accuracy when compared to either visual or volumetric ground truths.
Published: 2023
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21. Radiomics Outperforms Clinical and Radiologic Signs in Predicting Spontaneous Basal Ganglia Hematoma Expansion: A Pilot Study

Author: Houman Sotoudeh, Ali Rezaei, Ryan Godwin, Veeranjaneyulu Prattipati, Aparna Singhal, Mahsan Sotoudeh, and Manoj Tanwar
Subjects: General Engineering
Published: 2023

22. Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study.

Author: Emma Nichols, Foad Abd-Allah, Amir Abdoli, Ahmed Abualhasan, Eman Abu-Gharbieh, Ashkan Afshin, Rufus Akinyemi, Fahad Mashhour Alanezi, Vahid Alipour, Amir Almasi-Hashiani, Jalal Arabloo, Amir Ashraf-Ganjouei, Getinet Ayano, José Luis Ayuso-Mateos, Atif Amin Baig, Maciej Banach, Miguel A. Barboza, Suzanne Lyn Barker-Collo, Bernhard T. Baune, Akshaya Srikanth Bhagavathula, Krittika Bhattacharyya, Ali Bijani, Atanu Biswas, Archith Boloor, Carol Brayne, Hermann Brenner, Katrin Burkart, Sharath Burugina Nagaraja, Felix Carvalho, Luis F. S. Castro-de-Araujo, Ferrán Catalá-López, Ester Cerin, Nicolas Cherbuin, Dinh-Toi Chu, Xiaochen Dai, Antonio Reis de Sá-Junior, Shirin Djalalinia, Abdel Douiri, David Edvardsson, Shaimaa I. El-Jaafary, Sharareh Eskandarieh, Andre Faro, Farshad Farzadfar, Valery Feigin, Seyed-Mohammad Fereshtehnejad, Eduarda Fernandes, Pietro Ferrara 0002, Irina Filip, Florian Fischer, Shilpa Gaidhane, Lucia Galluzzo, Gebreamlak Gebremedhn Gebremeskel, Ahmad Ghashghaee, Alessandro Gialluisi, Elena V. Gnedovskaya, Mahaveer Golechha, Rajeev Gupta, Vladimir Hachinski, Mohammad R. Haider, Teklehaimanot Gereziher Haile, Mohammad Hamiduzzaman, Graeme J. Hankey, Simon I. Hay, Golnaz Heidari, Reza Heidari-Soureshjani, Hung Chak Ho, Mowafa S. Househ, Bing-Fang Hwang, Licia Iacoviello, Olayinka Stephen Ilesanmi, Irena M. Ilic, Milena D. Ilic, Seyed Sina Naghibi Irvani, Masao Iwagami, Ihoghosa Osamuyi Iyamu, Ravi Prakash Jha, Rizwan Kalani, André Karch, Ayele Semachew Kasa, Yousef S. Khader, Ejaz Ahmad Khan, Mahalaqua Nazli Khatib, Yun Jin Kim, Sezer Kisa, Adnan Kisa, Mika Kivimäki, Ai Koyanagi, Manasi Kumar, Iván Landires, Savita Lasrado, Bingyu Li, Stephen S. Lim, Xuefeng Liu, Shilpashree Madhava Kunjathur, Azeem Majeed, Preeti Malik, Man Mohan Mehndiratta, Ritesh G. Menezes, Yousef Mohammad, Salahuddin Mohammed, Ali H. Mokdad, Mohammad Ali Moni, Gabriele Nagel, Muhammad Naveed 0005, Vinod C. Nayak, Cuong Tat Nguyen, Thi Lan Huong Nguyen, Virginia Nunez-Samudio, Andrew T. Olagunju, Samuel M. Ostroff, Nikita Otstavnov, Mayowa Owolabi, Fatemeh Pashazadeh Kan, Urvish K. Patel, Michael R. Phillips, Michael A. Piradov, Constance Dimity Pond, Faheem Hyder Pottoo, Sergio I. Prada, Amir Radfar, Fakher Rahim, Juwel Rana, Vahid Rashedi, Salman Rawaf, David Laith Rawaf, Nickolas Reinig, Andre M. N. Renzaho, Nima Rezaei, Aziz Rezapour, Michele Romoli, Gholamreza Roshandel, Perminder S. Sachdev, Amirhossein Sahebkar, Mohammad Ali Sahraian, Mehrnoosh Samaei, Mete Saylan, Feng Sha, Masood Ali Shaikh, Kenji Shibuya, Mika Shigematsu, Jae Il Shin, Rahman Shiri, Diego Augusto Santos Silva, Jasvinder A. Singh, Deepika Singhal, Valentin Yurievich Skryabin, Anna Aleksandrovna Skryabina, Amin Soheili, Houman Sotoudeh, Emma Elizabeth Spurlock, Cassandra E. I. Szoeke, Rafael Tabarés-Seisdedos, Biruk Wogayehu Taddele, Marcos Roberto Tovani-Palone, Gebiyaw Wudie Tsegaye, Marco Vacante, Narayanaswamy Venketasubramanian, Simone Vidale, Vasily Vlassov, Giang Thu Vu, Yuan-Pang Wang, Jordan Weiss, Abrha Hailay Weldemariam, Ronny Westerman, Anders Wimo, Andrea Sylvia Winkler, Chenkai Wu, Ali Yadollahpour, Metin Yesiltepe, Naohiro Yonemoto, Chuanhua Yu, Mikhail Sergeevich Zastrozhin, Anasthasia Zastrozhina, Zhi-Jiang Zhang, Christopher J. L. Murray, and Theo Vos
Published: 2021
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23. Leveraging artificial intelligence in ischemic stroke imaging

Author: Ehsan Sotoudeh, Joshua D. Bernstock, Omid Shafaat, Vivek Yedavalli, Amir Shafaat, David M. Yousem, Saksham Gupta, Galal A. Elsayed, Houman Sotoudeh, and Haris I. Sair
Subjects: Diagnostic Imaging, Prognosis prediction, Infarction, Patient care, 030218 nuclear medicine & medical imaging, Machine Learning, Brain ischemia, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, medicine, Humans, Radiology, Nuclear Medicine and imaging, cardiovascular diseases, Stroke, Ischemic Stroke, Radiological and Ultrasound Technology, business.industry, Ischemic strokes, medicine.disease, Clinical diagnosis, Ischemic stroke, Neurology (clinical), Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract: Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.
Published: 2022

24. Diagnostic Performance of MRI Radiomics for Classification of Alzheimer’s disease, Mild Cognitive Impairment, and Normal Subjects: A Systematic Review and Meta-analysis

Author: Ramin Shahidi, Mansoureh Baradaran, Ali Asgarzadeh, Sara Bagherieh, Zohreh Tajabadi, Akram Farhadi, Setayesh Sotoudehnia Korani, Mohammad Khalafi, Parnian Shobeiri, Hamidreza Sadeghsalehi, Arezoo Shafieyoun, Mohammad Amin Yazdanifar, Aparna Singhal, and Houman Sotoudeh
Abstract: BackgroundAlzheimer’s disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task.Methods and materialsA comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to October 17, 2022. Original studies discussing the diagnostic performance of MRI Radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), and the Radiomic Quality Score tool (RQS).ResultsWe identified 10 studies that met the inclusion criteria, involving a total of 3446 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.8822 (95% CI 0.7888-0.9376), and 0.8849 (95% CI 0.7978-0.9374), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.7882 (95% CI 0.6272-0.8917) and 0.7736 (95% CI 0.6480-0.8639), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.6938 (95% CI 0.6465-0.7374) and 0.8173 (95% CI 0.6117-0.9270), respectively.ConclusionMRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.
Published: 2023

25. Olfactory Dysfunction in the COVID-19 Era: An Umbrella Review Focused on Neuroimaging, Management, and Follow-up

Author: Mohammadreza Kalantarhormozi, Houman Sotoudeh, Mohammad Amin Habibi, Mehdi Mahmudpour, Ramin Shahidi, Fattaneh Khalaj, Shaghayegh Karami, Ali Asgarzadeh, Mansoureh Baradaran, Fatemeh Chichagi, Sara Hassanzadeh, Narjes Sadat Farizani Gohari, Mahsa Shirforoush Sattari, Amir Azimi, Ali Dadjou, and Mahsan Eskandari
Abstract: PurposeThe coronavirus disease 2019 (COVID-19) is surrounded the world and is associated with multiorgan damage. Olfactory dysfunction is a common manifestation in COVID-19 patients, and in some cases, presents before the coryza signs. We conducted this umbrella review to provide a practical guide on managing, imaging findings, and follow-up of COVID-19 patients with olfactory dysfunction (OD).MethodsA comprehensive search was performed in PubMed, Embase, Scopus, and Web of Science databases from December 2019 until the end of July 2022. Systematic reviews and meta-analyses addressing management and imaging findings of the olfactory manifestations of COVID-19 were included in the study. The quality assessment of included articles was carried out using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) tool.ResultsA total of 23 systematic reviews were reviewed in this umbrella review. The number of included studies varied between 2 to 155 articles. Several demographic variables were not adequately reported across all the included systematic reviews, including age, gender, preexisting comorbidities, or whether participants had been hospitalized or admitted to the intensive care unit (ICU) due to COVIDLJ19.ConclusionIt seems that the coronavirus can infect olfactory system structures that play roles in the transmission and interpretation of smell sense. Based on studies, a large proportion of patients experienced OD following COVID-19 infection, and the majority of OD was resolved spontaneously. The possibility of long-lasting OD was higher in young adults with moderate clinical manifestation. Olfactory training (OT) was the most effective therapy. Intranasal corticosteroids (ICS) are also recommended.
Published: 2023

26. Orbital Myositis Mimicking Wall-Eyed Bilateral Internuclear Ophthalmoplegia

Author: Calvin McNelly, Houman Sotoudeh, and Lanning Kline
Subjects: Ophthalmology
Published: 2023

27. Clinical applications of diffusion-weighted sequence in brain imaging: beyond stroke

Author: Ali Rezaei, Siddhartha Gaddamanugu, Zahra Saadatpour, Houman Sotoudeh, Aparna Singhal, Omid Shafaat, and Amir Hossein Sarrami
Subjects: medicine.medical_specialty, Neurology, Brain tumors, Epilepsy, Review, Trauma, Brain Ischemia, Physical medicine and rehabilitation, Neuroimaging, Humans, Medicine, Radiology, Nuclear Medicine and imaging, cardiovascular diseases, Stroke, Neuroradiology, Sequence (medicine), Demyelinations, business.industry, Brain, medicine.disease, Magnetic Resonance Imaging, Diffusion imaging, Diffusion Magnetic Resonance Imaging, Diffusion-weighted imaging, Neurology (clinical), Neurosurgery, Infection, Cardiology and Cardiovascular Medicine, business, Diffusion MRI
Abstract: Diffusion-weighted imaging (DWI) is a well-established MRI sequence for diagnosing early stroke and provides therapeutic implications. However, DWI yields pertinent information in various other brain pathologies and helps establish a specific diagnosis and management of other central nervous system disorders. Some of these conditions can present with acute changes in neurological status and mimic stroke. This review will focus briefly on diffusion imaging techniques, followed by a more comprehensive description of the utility of DWI in common neurological entities beyond stroke.
Published: 2021

28. Head and Neck Squamous Cell Carcinoma Presenting With Leptomeningeal Carcinomatosis and Myeloradiculopathy via Perineural Growth

Author: Carmela V. San Luis, Sarah Breaux, Houman Sotoudeh, Kenneth Fallon, and Kenkichi Nozaki
Subjects: Neurology, Neurology (clinical), General Medicine
Published: 2023

29. Prediction of Spontaneous Basal Ganglia Hematoma Expansion; Clinical Data Versus Radiology Signs Versus Radiomics; A Pilot Study

Author: Ali Rezaei, Ryan Godwin, Veeranjaneyulu Prattipati, Manoj Tanwar, Aparna Singhal, Mahsan Sotoudeh, and Houman Sotoudeh
Abstract: Prediction the hematoma expansion (HE) of spontaneous basal ganglia hematoma (SBH) from the first non-contrast CT can improve the patient outcome. This study has been design to compare the performance of “Radiomics analysis”, “radiology signs”, and “clinical-aboratory data” for this task. We collected clinical, demographic, and laboratory from medical records in patients with SBH. CT images reviewed for the presence of radiologic signs, including Black hole, Blend, Swirl, Satellite, and Island signs, which may predict the hematoma expansion. Radiomic features from the SBH on the first brain CT were extracted, and the most predictive features were selected. Different machine learning models were developed based on clinical, laboratory, and radiology signs and selected Radiomic features to predict HE. The dataset used for this analysis included 116 patients with SBH. Among different models and different thresholds to define hematoma expansion (10%, 20%, 25%, 33%, 40%, and 50% volume enlargement thresholds), the Random Forest based on ten selected Radiomic features achieved the best performance (for 25% hematoma enlargement) with AUC of 0.9 on the training dataset and 0.89 on the test dataset. The models based on clinical-laboratory, and radiology signs had deficient performance (AUCs about 0.5–0.6). Base on our findings, Radiomic models outperform the radiology signs, clinical, laboratory, and demographic features to predict the HE. A twenty-five percent increase in the hematoma volume is likely the best threshold to predict HE by machine learning and Radiomics. The rondom forest models are probably the best models for this task.
Published: 2022

30. Cerebral Venous Sinus Thrombosis following Ventriculoperitoneal Shunting for Idiopathic Intracranial Hypertension

Author: Houman Sotoudeh, James M. Markert, Borna E. Tabibian, Lanning B. Kline, and John P. Luckett
Subjects: Ophthalmology, medicine.medical_specialty, Ventriculoperitoneal shunting, business.industry, Internal medicine, medicine, Cardiology, Case Reports, Neurology (clinical), Cerebral venous sinus thrombosis, medicine.disease, business
Abstract: A 30-year-old woman with idiopathic intracranial hypertension experienced worsening headaches and decreasing vision in her left eye. She underwent an uncomplicated ventriculoperitoneal shunt procedure but the following day was found to have cerebral venous sinus thrombosis. Treatment included venous sinus thrombectomy and anticoagulation. She had a favourable clinical outcome. Extensive evaluation including testing for thrombophilia was unremarkable. Potential causes for this rare association are discussed.
Published: 2021

31. Radiological and Clinical Findings of Non-bifurcating Carotid Artery With Aberrant Course: A Case Report and Review of the Literature

Author: Anas U Khan, Houman Sotoudeh, Siddhartha Gaddamanugu, Omid Shafaat, Celene Hadley, and Aparna Singhal
Subjects: General Engineering
Published: 2022

32. The global burden of cancer attributable to risk factors, 2010–19 : A systematic analysis for the Global Burden of Disease Study 2019

Author: Khanh Bao Tran, Justin J Lang, Kelly Compton, Rixing Xu, Alistair R Acheson, Hannah Jacqueline Henrikson, Jonathan M Kocarnik, Louise Penberthy, Amirali Aali, Qamar Abbas, Behzad Abbasi, Mohsen Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Hedayat Abbastabar, Michael Abdelmasseh, Sherief Abd-Elsalam, Ahmed Abdelwahab Abdelwahab, Gholamreza Abdoli, Hanan Abdulkadir Abdulkadir, Aidin Abedi, Kedir Hussein Abegaz, Hassan Abidi, Richard Gyan Aboagye, Hassan Abolhassani, Abdorrahim Absalan, Yonas Derso Abtew, Hiwa Abubaker Ali, Eman Abu-Gharbieh, Basavaprabhu Achappa, Juan Manuel Acuna, Daniel Addison, Isaac Yeboah Addo, Oyelola A Adegboye, Miracle Ayomikun Adesina, Mohammad Adnan, Qorinah Estiningtyas Sakilah Adnani, Shailesh M Advani, Sumia Afrin, Muhammad Sohail Afzal, Manik Aggarwal, Bright Opoku Ahinkorah, Araz Ramazan Ahmad, Rizwan Ahmad, Sajjad Ahmad, Sohail Ahmad, Sepideh Ahmadi, Haroon Ahmed, Luai A Ahmed, Muktar Beshir Ahmed, Tarik Ahmed Rashid, Wajeeha Aiman, Marjan Ajami, Gizachew Taddesse Akalu, Mostafa Akbarzadeh-Khiavi, Addis Aklilu, Maxwell Akonde, Chisom Joyqueenet Akunna, Hanadi Al Hamad, Fares Alahdab, Fahad Mashhour Alanezi, Turki M Alanzi, Saleh Ali Alessy, Abdelazeem M Algammal, Mohammed Khaled Al-Hanawi, Robert Kaba Alhassan, Beriwan Abdulqadir Ali, Liaqat Ali, Syed Shujait Ali, Yousef Alimohamadi, Vahid Alipour, Syed Mohamed Aljunid, Motasem Alkhayyat, Sadeq Ali Ali Al-Maweri, Sami Almustanyir, Nivaldo Alonso, Shehabaldin Alqalyoobi, Rajaa M Al-Raddadi, Rami H Hani Al-Rifai, Salman Khalifah Al-Sabah, Ala'a B Al-Tammemi, Haya Altawalah, Nelson Alvis-Guzman, Firehiwot Amare, Edward Kwabena Ameyaw, Javad Javad Aminian Dehkordi, Mohammad Hosein Amirzade-Iranaq, Hubert Amu, Ganiyu Adeniyi Amusa, Robert Ancuceanu, Jason A Anderson, Yaregal Animut Animut, Amir Anoushiravani, Ali Arash Anoushirvani, Alireza Ansari-Moghaddam, Mustafa Geleto Ansha, Benny Antony, Maxwell Hubert Antwi, Sumadi Lukman Anwar, Razique Anwer, Anayochukwu Edward Anyasodor, Jalal Arabloo, Morteza Arab-Zozani, Olatunde Aremu, Ayele Mamo Argaw, Hany Ariffin, Timur Aripov, Muhammad Arshad, Al Artaman, Judie Arulappan, Raphael Taiwo Aruleba, Armin Aryannejad, Malke Asaad, Mulusew A Asemahagn, Zatollah Asemi, Mohammad Asghari-Jafarabadi, Tahira Ashraf, Reza Assadi, Mohammad Athar, Seyyed Shamsadin Athari, Maha Moh'd Wahbi Atout, Sameh Attia, Avinash Aujayeb, Marcel Ausloos, Leticia Avila-Burgos, Atalel Fentahun Awedew, Mamaru Ayenew Awoke, Tewachew Awoke, Beatriz Paulina Ayala Quintanilla, Tegegn Mulatu Ayana, Solomon Shitu Ayen, Davood Azadi, Sina Azadnajafabad, Saber Azami-Aghdash, Melkalem Mamuye Azanaw, Mohammadreza Azangou-Khyavy, Amirhossein Azari Jafari, Hosein Azizi, Ahmed Y Y Azzam, Amirhesam Babajani, Muhammad Badar, Ashish D Badiye, Nayereh Baghcheghi, Nader Bagheri, Sara Bagherieh, Saeed Bahadory, Atif Amin Baig, Jennifer L Baker, Ahad Bakhtiari, Ravleen Kaur Bakshi, Maciej Banach, Indrajit Banerjee, Mainak Bardhan, Francesco Barone-Adesi, Fabio Barra, Amadou Barrow, Nasir Z Bashir, Azadeh Bashiri, Saurav Basu, Abdul-Monim Mohammad Batiha, Aeysha Begum, Alehegn Bekele Bekele, Alemayehu Sayih Belay, Melaku Ashagrie Belete, Uzma Iqbal Belgaumi, Arielle Wilder Bell, Luis Belo, Habib Benzian, Alemshet Yirga Berhie, Amiel Nazer C Bermudez, Eduardo Bernabe, Akshaya Srikanth Bhagavathula, Neeraj Bhala, Bharti Bhandari Bhandari, Nikha Bhardwaj, Pankaj Bhardwaj, Krittika Bhattacharyya, Vijayalakshmi S Bhojaraja, Soumitra S Bhuyan, Sadia Bibi, Awraris Hailu Bilchut, Bagas Suryo Bintoro, Antonio Biondi, Mesfin Geremaw Birega Birega, Habitu Eshetu Birhan, Tone Bjørge, Oleg Blyuss, Belay Boda Abule Bodicha, Srinivasa Rao Bolla, Archith Boloor, Cristina Bosetti, Dejana Braithwaite, Michael Brauer, Hermann Brenner, Andrey Nikolaevich Briko, Nikolay Ivanovich Briko, Christina Maree Buchanan, Norma B Bulamu, Maria Teresa Bustamante-Teixeira, Muhammad Hammad Butt, Nadeem Shafique Butt, Zahid A Butt, Florentino Luciano Caetano dos Santos, Luis Alberto Cámera, Chao Cao, Yin Cao, Giulia Carreras, Márcia Carvalho, Francieli Cembranel, Ester Cerin, Promit Ananyo Chakraborty, Periklis Charalampous, Vijay Kumar Chattu, Odgerel Chimed-Ochir, Jesus Lorenzo Chirinos-Caceres, Daniel Youngwhan Cho, William C S Cho, Devasahayam J Christopher, Dinh-Toi Chu, Isaac Sunday Chukwu, Aaron J Cohen, Joao Conde, Sandra Cortés, Vera Marisa Costa, Natália Cruz-Martins, Garland T Culbreth, Omid Dadras, Fentaw Teshome Dagnaw, Saad M A Dahlawi, Xiaochen Dai, Lalit Dandona, Rakhi Dandona, Parnaz Daneshpajouhnejad, Anna Danielewicz, An Thi Minh Dao, Reza Darvishi Cheshmeh Soltani, Aso Mohammad Darwesh, Saswati Das, Dragos Virgil Davitoiu, Elham Davtalab Esmaeili, Fernando Pio De la Hoz, Sisay Abebe Debela, Azizallah Dehghan, Biniyam Demisse, Fitsum Wolde Demisse, Edgar Denova-Gutiérrez, Afshin Derakhshani, Meseret Derbew Molla, Diriba Dereje, Kalkidan Solomon Deribe, Rupak Desai, Markos Desalegn Desalegn, Fikadu Nugusu Dessalegn, Samuel Abebe A Dessalegni, Gashaw Dessie, Abebaw Alemayehu Desta, Syed Masudur Rahman Dewan, Samath Dhamminda Dharmaratne, Meghnath Dhimal, Mostafa Dianatinasab, Nancy Diao, Daniel Diaz, Lankamo Ena Digesa, Shilpi Gupta Dixit, Saeid Doaei, Linh Phuong Doan, Paul Narh Doku, Deepa Dongarwar, Wendel Mombaque dos Santos, Tim Robert Driscoll, Haneil Larson Dsouza, Oyewole Christopher Durojaiye, Sareh Edalati, Fatemeh Eghbalian, Elham Ehsani-Chimeh, Ebrahim Eini, Michael Ekholuenetale, Temitope Cyrus Ekundayo, Donatus U Ekwueme, Maha El Tantawi, Mostafa Ahmed Elbahnasawy, Iffat Elbarazi, Hesham Elghazaly, Muhammed Elhadi, Waseem El-Huneidi, Mohammad Hassan Emamian, Luchuo Engelbert Bain, Daniel Berhanie Enyew, Ryenchindorj Erkhembayar, Tegegne Eshetu, Babak Eshrati, Sharareh Eskandarieh, Juan Espinosa-Montero, Farshid Etaee, Azin Etemadimanesh, Tahir Eyayu, Ifeanyi Jude Ezeonwumelu, Sayeh Ezzikouri, Adeniyi Francis Fagbamigbe, Saman Fahimi, Ildar Ravisovich Fakhradiyev, Emerito Jose A Faraon, Jawad Fares, Abbas Farmany, Umar Farooque, Hossein Farrokhpour, Abidemi Omolara Fasanmi, Ali Fatehizadeh, Wafa Fatima, Hamed Fattahi, Ginenus Fekadu, Berhanu Elfu Feleke, Allegra Allegra Ferrari, Simone Ferrero, Lorenzo Ferro Desideri, Irina Filip, Florian Fischer, Roham Foroumadi, Masoud Foroutan, Takeshi Fukumoto, Peter Andras Gaal, Mohamed M Gad, Muktar A Gadanya, Abduzhappar Gaipov, Nasrin Galehdar, Silvano Gallus, Tushar Garg, Mariana Gaspar Fonseca, Yosef Haile Gebremariam, Teferi Gebru Gebremeskel, Mathewos Alemu Gebremichael, Yohannes Fikadu Geda, Yibeltal Yismaw Gela, Belete Negese Belete Gemeda, Melaku Getachew, Motuma Erena Getachew, Kazem Ghaffari, Mansour Ghafourifard, Seyyed-Hadi Ghamari, Mohammad Ghasemi Nour, Fariba Ghassemi, Ajnish Ghimire, Nermin Ghith, Maryam Gholamalizadeh, Jamshid Gholizadeh Navashenaq, Sherief Ghozy, Syed Amir Gilani, Paramjit Singh Gill, Themba G Ginindza, Abraham Tamirat T Gizaw, James C Glasbey, Justyna Godos, Amit Goel, Mahaveer Golechha, Pouya Goleij, Davide Golinelli, Mohamad Golitaleb, Giuseppe Gorini, Bárbara Niegia Garcia Goulart, Giuseppe Grosso, Habtamu Alganeh Guadie, Mohammed Ibrahim Mohialdeen Gubari, Temesgen Worku Gudayu, Maximiliano Ribeiro Guerra, Damitha Asanga Gunawardane, Bhawna Gupta, Sapna Gupta, Veer Bala Gupta, Vivek Kumar Gupta, Mekdes Kondale Gurara, Alemu Guta, Parham Habibzadeh, Atlas Haddadi Avval, Nima Hafezi-Nejad, Adel Hajj Ali, Arvin Haj-Mirzaian, Esam S Halboub, Aram Halimi, Rabih Halwani, Randah R Hamadeh, Sajid Hameed, Samer Hamidi, Asif Hanif, Sanam Hariri, Netanja I Harlianto, Josep Maria Haro, Risky Kusuma Hartono, Ahmed I Hasaballah, S M Mahmudul Hasan, Hamidreza Hasani, Seyedeh Melika Hashemi, Abbas M Hassan, Soheil Hassanipour, Khezar Hayat, Golnaz Heidari, Mohammad Heidari, Zahra Heidarymeybodi, Brenda Yuliana Herrera-Serna, Claudiu Herteliu, Kamal Hezam, Yuta Hiraike, Mbuzeleni Mbuzeleni Hlongwa, Ramesh Holla, Marianne Holm, Nobuyuki Horita, Mohammad Hoseini, Md Mahbub Hossain, Mohammad Bellal 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Jayaram, S, Jazayeri, S, Jebai, R, Jemal, B, Jeong, W, Jha, R, Jindal, H, John-Akinola, Y, Jonas, J, Joo, T, Joseph, N, Joukar, F, Jozwiak, J, Jurisson, M, Kabir, A, Kacimi, S, Kadashetti, V, Kahe, F, Kakodkar, P, Kalankesh, L, Kalhor, R, Kamal, V, Kamangar, F, Kamath, A, Kanchan, T, Kandaswamy, E, Kandel, H, Kang, H, Kanno, G, Kapoor, N, Kar, S, Karanth, S, Karaye, I, Karch, A, Karimi, A, Kassa, B, Katoto, P, Kauppila, J, Kaur, H, Kebede, A, Keikavoosi-Arani, L, Kejela, G, Kemp Bohan, P, Keramati, M, Keykhaei, M, Khajuria, H, Khan, A, Khan, E, Khan, G, Khan, M, Khanali, J, Khatab, K, Khatatbeh, M, Khatib, M, Khayamzadeh, M, Khayat Kashani, H, Khazeei Tabari, M, Khezeli, M, Khodadost, M, Kim, M, Kim, Y, Kisa, A, Kisa, S, Klugar, M, Klugarova, J, Kolahi, A, Kolkhir, P, Kompani, F, Koul, P, Koulmane Laxminarayana, S, Koyanagi, A, Krishan, K, Krishnamoorthy, Y, Kucuk Bicer, B, Kugbey, N, Kulimbet, M, Kumar, A, Kumar, G, Kumar, N, Kurmi, O, Kuttikkattu, A, La Vecchia, C, Lahiri, A, Lal, D, Lam, J, Lan, Q, Landires, I, Larijani, B, Lasrado, S, Lau, J, Lauriola, P, Ledda, C, Lee, S, Lee, W, Lee, Y, Legesse, S, Leigh, J, Leong, E, Li, M, Lim, S, Liu, G, Liu, J, Lo, C, Lohiya, A, Lopukhov, P, Lorenzovici, L, Lotfi, M, Loureiro, J, Lunevicius, R, Madadizadeh, F, Mafi, A, Magdeldin, S, Mahjoub, S, Mahmoodpoor, A, Mahmoudi, M, Mahmoudimanesh, M, Mahumud, R, Majeed, A, Majidpoor, J, Makki, A, Makris, K, Malakan Rad, E, Malekpour, M, Malekzadeh, R, Malik, A, Mallhi, T, Mallya, S, Mamun, M, Manda, A, Mansour-Ghanaei, F, Mansouri, B, Mansournia, M, Mantovani, L, Martini, S, Martorell, M, Masoudi, S, Masoumi, S, Matei, C, Mathews, E, Mathur, M, Mathur, V, Mckee, M, Meena, J, Mehmood, K, Mehrabi Nasab, E, Mehrotra, R, Melese, A, Mendoza, W, Menezes, R, Mengesha, S, Mensah, L, Mentis, A, Mera-Mamian, A, Meretoja, T, Merid, M, Mersha, A, Meselu, B, Meshkat, M, Mestrovic, T, Miao Jonasson, J, Miazgowski, T, Michalek, I, Mijena, G, Miller, T, Mir, S, Mirinezhad, S, Mirmoeeni, S, Mirza-Aghazadeh-Attari, M, Mirzaei, H, Misganaw, A, Misra, S, Mohammad, K, Mohammadi, E, Mohammadi, M, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, A, Mohammed, S, Mohan, S, Mohseni, M, Moka, N, Mokdad, A, Molassiotis, A, Molokhia, M, Momenzadeh, K, Momtazmanesh, S, Monasta, L, Mons, U, Montasir, A, Montazeri, F, Montero, A, Moosavi, M, Moradi, A, Moradi, Y, Moradi Sarabi, M, Moraga, P, Morawska, L, Morrison, S, Morze, J, Mosapour, A, Mostafavi, E, Mousavi, S, Mousavi Isfahani, H, Mousavi Khaneghah, A, Mpundu-Kaambwa, C, Mubarik, S, Mulita, F, Munblit, D, Munro, S, Murillo-Zamora, E, Musa, J, Nabhan, A, Nagarajan, A, Nagaraju, S, Nagel, G, Naghipour, M, Naimzada, M, Nair, T, Naqvi, A, Narasimha Swamy, S, Narayana, A, Nassereldine, H, Natto, Z, Nayak, B, Ndejjo, R, Nduaguba, S, Negash, W, Nejadghaderi, S, Nejati, K, Neupane Kandel, S, Nguyen, H, Niazi, R, Noor, N, Noori, M, Noroozi, N, Nouraei, H, Nowroozi, A, Nunez-Samudio, V, Nzoputam, C, Nzoputam, O, Oancea, B, Odukoya, O, Oghenetega, O, Ogunsakin, R, Oguntade, A, Oh, I, Okati-Aliabad, H, Okekunle, A, Olagunju, A, Olagunju, T, Olakunde, B, Olufadewa, I, Omer, E, Omonisi, A, Ong, S, Onwujekwe, O, Orru, H, Otstavnov, S, Oulhaj, A, Oumer, B, Owopetu, O, Oyinloye, B, P A, M, Padron-Monedero, A, Padubidri, J, Pakbin, B, Pakshir, K, Pakzad, R, Palicz, T, Pana, A, Pandey, A, Pant, S, Pardhan, S, Park, E, Park, S, Patel, J, Pati, S, Paudel, R, Paudel, U, Paun, M, Pazoki Toroudi, H, Peng, M, Pereira, J, Pereira, R, Perna, S, Perumalsamy, N, Pestell, R, Pezzani, R, Piccinelli, C, Pillay, J, Piracha, Z, Pischon, T, Postma, M, Pourabhari Langroudi, A, Pourshams, A, Pourtaheri, N, Prashant, A, Qadir, M, Quazi Syed, Z, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, R, Radhakrishnan, V, Raeisi, M, Rafiee, A, Rafiei, A, Raheem, N, Rahim, F, Rahman, M, Rahmani, A, Rahmani, S, Rahmanian, V, Rajai, N, Rajesh, A, Ram, P, Ramezanzadeh, K, Rana, J, Ranabhat, K, Ranasinghe, P, Rao, C, Rao, S, Rashedi, S, Rashidi, A, Rashidi, M, Ratan, Z, Rawaf, D, Rawaf, S, Rawal, L, Rawassizadeh, R, Razeghinia, M, Rehman, A, Rehman, I, Reitsma, M, Renzaho, A, Rezaei, M, Rezaei, N, Rezaei, S, Rezaeian, M, Rezapour, A, Riad, A, Rikhtegar, R, Rios-Blancas, M, Roberts, T, Rohloff, P, Romero-Rodriguez, E, Roshandel, G, Rwegerera, G, S, M, Saber-Ayad, M, Saberzadeh-Ardestani, B, Sabour, S, Saddik, B, Sadeghi, E, Saeb, M, Saeed, U, Safaei, M, Safary, A, Sahebazzamani, M, Sahebkar, A, Sahoo, H, Sajid, M, Salari, H, Salehi, S, Salem, M, Salimzadeh, H, Samodra, Y, Samy, A, Sanabria, J, Sankararaman, S, Sanmarchi, F, Santric-Milicevic, M, Saqib, M, Sarveazad, A, Sarvi, F, Sathian, B, Satpathy, M, Sayegh, N, Schneider, I, Schwarzinger, M, Sekerija, M, Senthilkumaran, S, Sepanlou, S, Seylani, A, Seyoum, K, Sha, F, Shafaat, O, Shah, P, Shahabi, S, Shahid, I, Shahrbaf, M, Shahsavari, H, Shaikh, M, Shaka, M, Shaker, E, Shannawaz, M, Sharew, M, Sharifi, A, Sharifi-Rad, J, Sharma, P, Shashamo, B, Sheikh, A, Sheikh, M, Sheikhbahaei, S, Sheikhi, R, Sheikhy, A, Shepherd, P, Shetty, A, Shetty, J, Shetty, R, Shibuya, K, Shirkoohi, R, Shirzad-Aski, H, Shivakumar, K, Shivalli, S, Shivarov, V, Shobeiri, P, Shokri Varniab, Z, Shorofi, S, Shrestha, S, Sibhat, M, Siddappa Malleshappa, S, Sidemo, N, Silva, D, Silva, L, Silva Julian, G, Silvestris, N, Simegn, W, Singh, A, Singh, G, Singh, H, Singh, J, Singh, P, Singh, S, Sinha, D, Sinke, A, Siraj, M, Sitas, F, Siwal, S, Skryabin, V, Skryabina, A, Socea, B, Soeberg, M, Sofi-Mahmudi, A, Solomon, Y, Soltani-Zangbar, M, Song, S, Song, Y, Sorensen, R, Soshnikov, S, Sotoudeh, H, Sowe, A, Sufiyan, M, Suk, R, Suleman, M, Suliankatchi Abdulkader, R, Sultana, S, Sur, D, Szocska, M, Tabaeian, S, Tabares-Seisdedos, R, Tabatabaei, S, Tabuchi, T, Tadbiri, H, Taheri, E, Taheri, M, Taheri Soodejani, M, Takahashi, K, Talaat, I, Tampa, M, Tan, K, Tat, N, Tat, V, Tavakoli, A, Tehrani-Banihashemi, A, Tekalegn, Y, Tesfay, F, Thapar, R, Thavamani, A, Thoguluva Chandrasekar, V, Thomas, N, Ticoalu, J, Tiyuri, A, Tollosa, D, Topor-Madry, R, Touvier, M, Tovani-Palone, M, Traini, E, Tran, M, Tripathy, J, Ukke, G, Ullah, I, Ullah, S, Unnikrishnan, B, Vacante, M, Vaezi, M, Valadan Tahbaz, S, Valdez, P, Vardavas, C, Varthya, S, Vaziri, S, Velazquez, D, Veroux, M, Villeneuve, P, Violante, F, Vladimirov, S, Vlassov, V, Vo, B, Vu, L, Wadood, A, Waheed, Y, Walde, M, Wamai, R, Wang, C, Wang, F, Wang, N, Wang, Y, Ward, P, Waris, A, Westerman, R, Wickramasinghe, N, Woldemariam, M, Woldu, B, Xiao, H, Xu, S, Xu, X, Yadav, L, Yahyazadeh Jabbari, S, Yang, L, Yazdanpanah, F, Yeshaw, Y, Yismaw, Y, Yonemoto, N, Younis, M, Yousefi, Z, Yousefian, F, Yu, C, Yu, Y, Yunusa, I, Zahir, M, Zaki, N, Zaman, B, Zangiabadian, M, Zare, F, Zare, I, Zareshahrabadi, Z, Zarrintan, A, Zastrozhin, M, Zeineddine, M, Zhang, D, Zhang, J, Zhang, Y, Zhang, Z, Zhou, L, Zodpey, S, Zoladl, M, Vos, T, Hay, S, Force, L, Murray, C, Epidemiologie, RS: NUTRIM - R3 - Respiratory & Age-related Health, Bill & Melinda Gates Foundation, Kuwait University (Kuwait), Ministry of Higher Education (Malasia), Lega Italiana per la Lotta ai Tumori, Health Effects Institute (Estados Unidos), Unión Europea. Comisión Europea. European Research Council (ERC), Unión Europea. Comisión Europea. H2020, Fundação para a Ciência e Tecnologia (Portugal), African-German Network of Excellence in Science (AGNES), Federal Ministry of Education & Research (Alemania), Alexander von Humboldt Foundation, Novo Nordisk Foundation, National Institute for Health Research (Reino Unido), National Health and Medical Research Council (Australia), Romanian National Authority for Scientific Research and Innovation, Romanian Ministry of Research Innovation and Digitalization, Ministry of Education, Science and Technological Development (Serbia), Sigrid Jusélius Foundation, Finnish Cancer Foundation, Datta Meghe Institute of Medical Sciences (India), Xiamen University (Malasia), Manipal Academy of Higher Education (India), Panjab University (India), Sistema Nacional de Investigación (Panamá), Secretaría Nacional de Ciencia, Tecnología e Innovación (Panamá), Ministry of Science and Technology (Taiwan), Lung Foundation Australia, National Natural Science Foundation of China, Wellcome Trust, UNSW Sydney (Australia), ICMR - National Institute of Epidemiology (India), University of Tasmania (Australia), National Council for Scientific and Technological Development (Brasil), Coordenação de Aperfeicoamento de Pessoal de Nível Superior (Brasil), Institute for Advanced Studies in Basic Sciences (Irán), Ain Shams University (Egipto), International Center of Medical Sciences Research (Islamabad), National Institutes of Health (Estados Unidos), University of Oxford (Reino Unido), National Institute of Genetic Engineering and Biotechnology (Irán), Marga und Walter Boll - Stiftung, Ministero della Salute (Italia), IRCCS Materno Infantile Burlo Garofolo (Italia), King College London, Wellcome Trust/DBT India Alliance (India), Public Health, University of St Andrews. School of Medicine, and University of St Andrews. Population and Behavioural Science Division
Subjects: Male, DEATHS, DALY, cancer, risk factors, Medizin, systematic analysis, Global Health, Risk Assessment, Cancer prevention, Global Burden of Disease, RC0254, Risk-attributable cancer deaths, SDG 3 - Good Health and Well-being, RA0421, Risk Factors, RA0421 Public health. Hygiene. Preventive Medicine, Quality-Adjusted Life Year, Neoplasms, cancer, Humans, Global Burden of Disease Study, UK, Medicine(all), MCC, RC0254 Neoplasms. Tumors. Oncology (including Cancer), Risk Factor, Smoking, COVID-19, 3rd-DAS, General Medicine, Disability-adjusted life-years, SOCIAL DETERMINANTS, Risk assessments, risk factor, Cardiovascular and Metabolic Diseases, 3121 General medicine, internal medicine and other clinical medicine, OBESITY, Cancer burden, Neoplasm, Female, LIFE-STYLE, Quality-Adjusted Life Years, HEALTH, RA, Human, RC
Abstract: Background: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01-4·94) deaths and 105 million (95·0-116) DALYs for both sexes combined, representing 44·4% (41·3-48·4) of all cancer deaths and 42·0% (39·1-45·6) of all DALYs. There were 2·88 million (2·60-3·18) risk-attributable cancer deaths in males (50·6% [47·8-54·1] of all male cancer deaths) and 1·58 million (1·36-1·84) risk-attributable cancer deaths in females (36·3% [32·5-41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6-28·4) and DALYs by 16·8% (8·8-25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9-42·8] and 33·3% [25·8-42·0]). Interpretation: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. We are grateful to the surveillance systems, including cancer registries, that generated and shared observed cancer burden data. S M Aljunid acknowledges the Department of Health Policy and Management, College of Public Health, Kuwait University for the approval and support to participate in this research project. H Ariffin acknowledges support from the Ministry of Higher Education, Malaysia (grant FRGS/1/2021/SKK0/UM/01/1). F Barra acknowledges support from Lega Italiana per la Lotta contro i Tumori - LILT - Bando 5 x 1000 anno 2019. L Belo and M Carvalho acknowledge the support from FCT in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of UCIBIO and the project LA/P/0140/2020 of i4HB. A J Cohen was supported by the Health Effects Institute, Boston, MA, USA. J Conde acknowledges financial support from the European Research Council - ERC Starting Grant 848325. V M Costa acknowledges her grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006. T C Ekundayo was supported by the African-German Network of Excellence in Science (AGNES), the Federal Ministry of Education and Research (BMBF) and the Alexander von Humboldt Foundation (AvH). N Ghith acknowledges support from a grant from Novo Nordisk Foundation (NNF16OC0021856). J C Glasbey is support by a Doctoral Research Fellowship from the National Institute of Health Research (NIHR300175). V K Gupta and V B Gupta acknowledge funding support from National Health and Medical Research Council (NHMRC), Australia. C Herteliu, A Pana, and M Ausloos acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. C Herteliu is also partially supported by a grant of the Romanian Ministry of Research Innovation and Digitalization, MCID, project number ID-585-CTR-42-PFE-2021. S Hussain was supported from Operational Programme Research, Development and Education–Project, Postdoc2MUNI (number CZ.02.2. 69/0.0/0.0/18_053/0016952). M Jakovljevic acknowledges partial support through the grant OI 175 014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. J H Kauppila acknowledges research grants from Sigrid Jusélius Foundation and the Finnish Cancer Foundation. M N Khatib acknowledges support from Datta Meghe Institute of Medical Sciences (deemed-to-be-university). Y J Kim was supported by the Research Management Centre, Xiamen University Malaysia [XMUMRF/2020-C6/ITCM/0004]. S L Koulmane Laxminarayana acknowledges institutional assistance by Manipal Academy of Higher Education, Manipal. K Krishan is supported by the UGC Centre of Advanced Study (Phase II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. I Landires is a member of the Sistema Nacional de Investigación (SNI), which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). M-C Li was supported by the Ministry of Science and Technology, Taiwan (MOST 110-2314-B-003-001). G Liu acknowledges support from the CREATE Hope scientific fellowship from Lung Foundation Australia. J Liu acknowledges support from the National Natural Science Foundation (72122001). J A Loureiro was supported by Scientific Employment Stimulus (FCT; CEECINST/00049/2018). E Mathews is supported by a Clinical and Public Health Early Career Fellowship (grant number IA/CPHE/17/1/503345) from the DBT India Alliance/Wellcome Trust Department of Biotechnology, India Alliance (2018–2023). T J Meretoja was supported by an unrestricted grant from Cancer Foundation Finland sr. S Mohammed acknowledges a fellowship grant from Alexander von Humboldt Foundation, outside the submitted work. M Molokhia is supported by the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’ National Health Service Foundation Trust and King’s College London. L Monasta received support from the Italian Ministry of Health at the Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste - Italy (RC 34/2017). U Mons is supported by the Marga and Walter Boll Foundation, Kerpen, Germany. M A Moosavi acknowledges the financial support of National Institute of Genetics Engineering and Biotechnology (NIGEB). J Musa acknowledges support from the NIH/FICK43TW011416 for research-protected time for cervical cancer research and career development at University of Jos. V Nuñez-Samudio is a member of the Sistema Nacional de Investigación (SNI), which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). O O Odukoya acknowledges support by the Fogarty International Center of the National Institutes of Health under the award number K43TW010704 for research-protected time. The content is solely the responsibility of all the authors and does not necessarily represent the official views of the National Institutes of Health. A S Oguntade acknowledges funding by a doctoral scholarship from the Nuffield Department of Population Health, University of Oxford (Oxford Population Health). J R Padubidri acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal for their constant support in research collaborations. R G Pestell acknowledges support from NIH grant W81XWH1810605 Breast Cancer Research, Breakthrough Grant R21 CA235139-01. Z Z Piracha acknowledges the International Center of Medical Sciences Research (ICMSR), Islamabad (44000), Pakistan. R A Radhakrishnan acknowledges support from Wellcome Trust/DBT India Alliance - IA/CPHI/18/1/503927. U Saeed acknowledges the International Center of Medical Sciences Research (ICMSR), Islamabad, Pakistan. A M Samy acknowledges the support from Ain Shams University and the Egyptian Fulbright Mission Program. F Sha was supported by the Shenzhen Science and Technology Program (grant number KQTD20190929172835662). H R Shahsavari acknowledges the Institute for Advanced Studies in Basic Sciences (IASBS) Research Council. A Shetty acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal for all the academic support. D A S Silva acknowledges financing in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001 and D A S Silva is supported in part by CNPq-Brazil (309589/2021-5). L M L R Silva was supported by project CENTRO-04-3559-FSE-000162, Fundo Social Europeu (FSE). Am Singh is supported by the International Graduate Research Scholarship, University of Tasmania. R Suliankatchi Abdulkader acknowledges support from ICMR—National Institute of Epidemiology. B Unnikrishnan acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal. H Xiao acknowledges support from the Public Health Sciences Division of the Fred Hutchinson Cancer Research Center. X Xu is supported by the University of New South Wales (Australia) Scientia Program. C Yu was supported by the National Natural Science Foundation of China (grant number 82173626) and Wuhan Medical Research Program of Joint Fund of Hubei Health Committee (grant number WJ2019H304). Sí
Published: 2022

33. Cerebellar Microstructural Abnormalities in Parkinson’s Disease: a Systematic Review of Diffusion Tensor Imaging Studies

Author: Maryam Haghshomar, Parnian Shobeiri, Seyed Arsalan Seyedi, Fatemeh Abbasi-Feijani, Amirhossein Poopak, Houman Sotoudeh, Arash Kamali, and Mohammad Hadi Aarabi
Subjects: Diffusion Tensor Imaging, Neurology, Cerebellum, White matter, Parkinson’s disease, Anisotropy, Humans, Neuroimaging, Parkinson Disease, Neurology (clinical), Diffusion tensor imaging, White Matter
Abstract: Diffusion tensor imaging (DTI) is now having a strong momentum in research to evaluate the neural fibers of the CNS. This technique can study white matter (WM) microstructure in neurodegenerative disorders, including Parkinson's disease (PD). Previous neuroimaging studies have suggested cerebellar involvement in the pathogenesis of PD, and these cerebellum alterations can correlate with PD symptoms and stages. Using the PRISMA 2020 framework, PubMed and EMBASE were searched to retrieve relevant articles. Our search revealed 472 articles. After screening titles and abstracts, and full-text review, and implementing the inclusion criteria, 68 papers were selected for synthesis. Reviewing the selected studies revealed that the patterns of reduction in cerebellum WM integrity, assessed by fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity measures can differ symptoms and stages of PD. Cerebellar diffusion tensor imaging (DTI) changes in PD patients with "postural instability and gait difficulty" are significantly different from "tremor dominant" PD patients. Freezing of the gate is strongly related to cerebellar involvement depicted by DTI. The "reduced cognition," "visual disturbances," "sleep disorders," "depression," and "olfactory dysfunction" are not related to cerebellum microstructural changes on DTI, while "impulsive-compulsive behavior" can be linked to cerebellar WM alteration. Finally, higher PD stages and longer disease duration are associated with cerebellum white matter alteration depicted by DTI. Depiction of cerebellar white matter involvement in PD is feasible by DTI. There is an association with disease duration and severity and several clinical presentations with DTI findings. This clinical-imaging association may eventually improve disease management.
Published: 2022

34. Intraventricular metastatic melanoma: A case report and review of the literature

Author: Travis J. Atchley, Mark R. Harrigan, Brandon M. Fox, Houman Sotoudeh, Gregory K. Friedman, Melissa Chua, Gustavo Chagoya, James R. Hackney, Nabiel Mir, Galal A. Elsayed, Saksham Gupta, Joshua D. Bernstock, and Mina Lobbous
Subjects: Oncology, medicine.medical_specialty, Metastatic melanoma, Improved survival, intraventricular metastasis, lcsh:Medicine, Case Report, Case Reports, 030204 cardiovascular system & hematology, Malignant disease, Resection, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, central nervous system metastasis, Adjuvant therapy, melanoma, Medicine, lcsh:R5-920, business.industry, Melanoma, lcsh:R, General Medicine, medicine.disease, 030220 oncology & carcinogenesis, business, lcsh:Medicine (General), metastatic melanoma
Abstract: Intraventricular melanoma is a very rare and highly malignant disease. Safe resection is the mainstay of treatment, but no standard guidelines exist for adjuvant therapy. Early histologic and molecular diagnosis is key for improved survival.
Published: 2020

35. Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses

Author: Mohsen Tabatabaei, Kamran Tavakol, Ehsan Sotoudeh, Houman Sotoudeh, Abdol Latif Moini, and Baharak Tasorian
Subjects: 0301 basic medicine, Diagnostic Imaging, Coronavirus disease 2019 (COVID-19), Viral pneumonia, Chest ct, Residual neural network, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Pandemic, medicine, Clinical significance, 030212 general & internal medicine, Original Paper, business.industry, Radiographic image, General Medicine, medicine.disease, Neural network, Pneumonia, 030104 developmental biology, COVID-19 virus, Computer tomographic, Artificial intelligence, business
Abstract: Background Given the current pandemic, differentiation between pneumonia induced by COVID-19 or influenza viruses is of utmost clinical significance in the patients' management. For this purpose, this study was conducted to develop sensitive artificial intelligence (AI) models to assist radiologists to decisively differentiate pneumonia due to COVID-19 versus influenza viruses. Methods Cross sectional chest CT images (N=12744) from well-evaluated cases of pneumonias induced by COVID-19 or H1N1 Influenza viruses, and normal individuals were collected. We examined the computer tomographic (CT) chest images from 137 individuals. Various pre-trained convolutional neural network models, such as ResNet-50, InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19 were fine-tuned on our datasets. The datasets were used for training (60%), validation (20%), and testing (20%) of the final models. Also, the predictive power and means of precision and recall were determined for each model. Results Fine-tuned ResNet-50 model differentiated the pneumonia due to COVID-19 or H1N1 influenza virus with accuracies of 96.7% and 92%, respectively This model outperformed all others, i.e., InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19. Conclusion Fine-tuned and pre-trained image classifying models of AI enable radiologists to reliably differentiate the pneumonia induced by COVID-19 versus H1N1 influenza virus. For this purpose, ResNet-50 followed by InceptionV3 models proved more promising than other AI models. Also in the supplements, we share the source codes and our fine-tuned models for use by researchers and clinicians globally toward the critical task of image differentiation of patients infected with COVID-19 versus H1N1 Influenza viruses.
Published: 2020

36. 18F-Sodium Fluoride PET: History, Technical Feasibility, Mechanism of Action, Normal Biodistribution, and Diagnostic Performance in Bone Metastasis Detection Compared with Other Imaging Modalities

Author: Ramandeep Singh, Samuel J. Galgano, Siddhartha Gaddamanugu, Houman Sotoudeh, Nishant Gupta, Gagandeep Choudhary, and Kriti Ahuja
Subjects: PET-CT, medicine.medical_specialty, Biodistribution, Radiobiology, Radiological and Ultrasound Technology, business.industry, Bone metastasis, General Medicine, medicine.disease, 030218 nuclear medicine & medical imaging, Imaging modalities, Metastasis, 03 medical and health sciences, 0302 clinical medicine, Osseous metastasis, Medicine, Radiology, Nuclear Medicine and imaging, Radiology, business, Radiation treatment planning
Abstract: The skeleton is the third most common site for metastasis overall, after the lungs and liver. Accurate diagnosis of osseous metastasis is critical for initial staging, treatment planning, restaging, treatment monitoring, and survival prediction. Currently, 99mTc-methylene diphosphonate whole-body scanning is the cornerstone of imaging to detect osseous metastasis. Although 18F-sodium fluoride (18F-NaF) was one of the oldest medical tracers for this purpose, it was replaced by other tracers because of their better physical properties, until recently. Continued development of PET scanners has opened a new era for 18F-NaF, and given its higher sensitivity, there have been increasing applications in imaging. In this review, we will discuss the history, technical aspects, radiobiology, and biodistribution of this tracer. Finally, we compare the accuracy of 18F-NaF PET with other conventional imaging methods for detection of osseous metastasis.
Published: 2019

37. Cauda equina syndrome in an ovarian malignant‐mixed müllerian tumor with leptomeningeal spread

Author: Stuart A. Ostby, Gregory K. Friedman, Warner K. Huh, Gustavo Chagoya, Andrew Janssen, Brandon M. Fox, Jason A. Chen, Yun Jee Kang, Houman Sotoudeh, Burt Nabors, Galal A. Elsayed, Veeranjaneyulu Prattipati, Joshua D. Bernstock, Daisuke Yamashita, and Mina Lobbous
Subjects: Oncology, endocrine system, medicine.medical_specialty, Poor prognosis, leptomeningeal carcinomatosis, lcsh:Medicine, Case Report, Cauda equina syndrome, Case Reports, 030204 cardiovascular system & hematology, Metastasis, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, metastasis, Medicine, In patient, Ovarian Carcinosarcoma, cauda equina syndrome, lcsh:R5-920, business.industry, Treatment regimen, lcsh:R, General Medicine, Ovarian Malignant Mixed Mullerian Tumor, medicine.disease, ovarian carcinosarcoma, ovarian malignant‐mixed müllerian tumor, 030220 oncology & carcinogenesis, lcsh:Medicine (General), business, Ovarian cancer
Abstract: Leptomeningeal metastasis is extremely rare in patients with ovarian cancer, but should be considered in patients presenting with neurologic deficits such as cauda equine syndrome. Given its poor prognosis and lack of data currently on management, additional studies are needed to optimize treatment regimens and improve outcomes.
Published: 2019

38. Emerging Applications of Radiomics in Neurological Disorders: A Review

Author: Houman Sotoudeh, Manoj Tanwar, Zahra Sadaatpour, Ehsan Sotoudeh, Glenn H. Roberson, Ali Rezaei, Omid Shafaat, Amir Hossein Sarrami, Aparna Singhal, and Gagandeep Choudhary
Subjects: medicine.medical_specialty, business.industry, General Engineering, Neurosurgery, artificial intelligence, stroke, demyelinating disorders, Radiomics, Neurology, radiomics, Medicine, business, Intensive care medicine, Radiology
Abstract: Radiomics has achieved significant momentum in radiology research and can reveal image information invisible to radiologists' eyes. Radiomics first evolved for oncologic imaging. Oncologic applications (histopathology, tumor grading, gene mutation analysis, patient survival, and treatment response prediction) of radiomics are widespread. However, it is not limited to oncologic analysis, and any digital medical images can benefit from radiomics analysis. This article reviews the current literature on radiomics in non-oncologic, neurological disorders including ischemic strokes, hemorrhagic stroke, cerebral aneurysms, and demyelinating disorders.
Published: 2021

39. Role of PET/MR in Squamous Cell Cancer Staging

Author: Houman Sotoudeh and Gagandeep Choudhary
Subjects: medicine.medical_specialty, Squamous cell cancer, business.industry, Medicine, Radiology, business
Published: 2021

40. Metastatic Disease in the Head and Neck

Author: Gagandeep Choudhary and Houman Sotoudeh
Subjects: medicine.medical_specialty, business.industry, medicine, Radiology, Disease, business, Head and neck
Published: 2021

41. The Role of Machine Learning and Radiomics for Treatment Response Prediction in Idiopathic Normal Pressure Hydrocephalus

Author: Houman Sotoudeh, Ali Rezaei, Mohsen Tabatabaie, Omid Shafaat, Zahra Sadaatpour, Aparna Singhal, Manoj Tanwar, and Ehsan Sotoudeh
Subjects: normal pressure hydrocephalus, medicine.diagnostic_test, business.industry, Medical record, General Engineering, Magnetic resonance imaging, artificial intelligence, medicine.disease, Machine learning, computer.software_genre, Random forest, Shunting, Support vector machine, machine learning, Radiomics, radiomics, Modified Rankin Scale, Normal pressure hydrocephalus, medicine, magnetic resonance imaging, Artificial intelligence, Radiology, business, computer
Abstract: Introduction Ventricular shunting remains the standard of care for patients with idiopathic normal pressure hydrocephalus (iNPH); however, not all patients benefit from the shunting. Prediction of response in advance can result in improved patient selection for ventricular shunting. This study aims to develop a machine learning predictive model for treatment response after shunt placement using the clinical and radiomics features. Methods In this retrospective pilot study, the medical records of iNPH patients who underwent ventricular shunting were evaluated. In each patient, the “idiopathic normal pressure hydrocephalus grading scale” (iNPHGS) and a “Modified Rankin Scale” were calculated before and after surgery. The subsequent treatment response was calculated as the difference between the iNPHGS scores before and after surgery. iNPHGS score reduction of two or more than two were considered as treatment response. The presurgical MRI scans were evaluated by radiologists, the ventricular systems were segmented on the T2-weighted images, and the radiomics features were extracted from the segmented ventricular system. Using Orange data mining open-source platform, different machine learning models were then developed based on the presurgical clinical features and the selected radiomics features to predict treatment response after shunt placement. Results After the implementation of the inclusion criteria, 78 patients were included in this study. One hundred twenty radiomics features were extracted, and the 12 best predictive radiomics features were selected. Using only clinical data (iNPHGS and Modified Rankin Scale), the random forest model achieved the best performance in treatment prediction with an area under the curve (AUC) of 0.71. Adding the Radiomics analysis to the clinical data improved the prediction performance, with the support vector machine (SVM) achieving the highest rank in treatment prediction with an AUC of 0.8. Adding age and sex to the analysis did not improve the prediction. Conclusion Using machine learning models for treatment response prediction in patients with iNPH is feasible with acceptable accuracy. Adding the Radiomics analysis to the clinical features can further improve the predictive performance. SVM is likely the best model for this task.
Published: 2021

42. Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission

Author: Omid Shafaat, Mojtaba Didehdar, Amir Hossein Sarrami, Mohsen Tabatabaie, Houman Sotoudeh, and Baharak Tasorian
Subjects: 2019-20 coronavirus outbreak, Artificial neural network, Ensemble forecasting, Coronavirus disease 2019 (COVID-19), business.industry, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), General Engineering, Feature selection, Infectious Disease, pandemics, Machine learning, computer.software_genre, artificial intelligence, mortality, Test (assessment), Naive Bayes classifier, machine learning, covid-19, Epidemiology/Public Health, Internal Medicine, Medicine, Artificial intelligence, prognosis, business, computer
Abstract: Aim This study aimed to develop a predictive model to predict patients' mortality with coronavirus disease 2019 (COVID-19) from the basic medical data on the first day of admission. Methods The medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset. Results A total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset. Conclusion Developing a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.
Published: 2021

43. Reply to the letter: 'Letter to the editor regarding differentiating fat embolism from critical illness associated cerebral microbleeds.'

Author: Aparna Singhal, Philip R. Chapman, Glenn H. Roberson, and Houman Sotoudeh
Subjects: medicine.medical_specialty, Letter to the editor, business.industry, Critical Illness, Embolism, Fat, medicine.disease, Critical illness, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Fat embolism, business, Intensive care medicine, Pulmonary Embolism, Cerebral Hemorrhage
Published: 2021

44. Skull base osteomyelitis imaging

Author: Philip R. Chapman, Houman Sotoudeh, and Aparna Singhal
Subjects: Diagnostic Imaging, Skull Base, medicine.medical_specialty, Treatment response, Modalities, business.industry, Context (language use), Osteomyelitis, Skull base osteomyelitis, Disease course, Imaging modalities, Otorhinolaryngology, medicine, Humans, Surgery, Medical physics, Medical diagnosis, business, Retrospective Studies
Abstract: Purpose of review Skull base osteomyelitis (SBO) is a challenging entity to diagnose and treat. The goal of this review is to summarize the imaging findings of SBO and present these in the context of recent studies on imaging of SBO. Recent findings This review discusses the clinical presentation, pathophysiology and imaging appearances of SBO. The review further emphasizes the results of latest studies on imaging of SBO, and the role of different modalities in diagnosis and evaluation of disease course and treatment response. Brief discussion on differential diagnoses from an imaging standpoint is also included. Summary Various imaging modalities play different and complimentary roles in diagnosis and management of SBO, which are discussed in this review.
Published: 2021

45. The Role of Medical Imaging in COVID-19

Author: Houman, Sotoudeh and Masoumeh, Gity
Subjects: SARS-CoV-2, COVID-19, Humans, Radiography, Thoracic, Tomography, X-Ray Computed, Pandemics
Abstract: The coronavirus disease 2019 (COVID-19) pandemic launched in the third decade of the twenty-first century and continued to present time to cause the worst challenges the modern medicine has ever encountered. Medical imaging is an essential part of the universal fight against this pandemic. In the absence of documented treatment and vaccination, early accurate diagnosis of infected patients is the backbone of this pandemic management. This chapter reviews different aspects of medical imaging in the context of COVID-19.
Published: 2021

46. Misleading CT perfusion in subacute ischemic stroke

Author: Omid Shafaat, Ehsan Sotoudeh, and Houman Sotoudeh
Subjects: Male, medicine.medical_specialty, Subacute phase, Computed Tomography Angiography, Infarction, Perfusion scanning, Conservative Treatment, Brain Ischemia, 030218 nuclear medicine & medical imaging, Diagnosis, Differential, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Radiology, Nuclear Medicine and imaging, cardiovascular diseases, Radiation treatment planning, Stroke, medicine.diagnostic_test, business.industry, Penumbra, 030208 emergency & critical care medicine, Magnetic resonance imaging, Middle Aged, medicine.disease, Ischemic stroke, Emergency Medicine, Radiographic Image Interpretation, Computer-Assisted, Radiology, Tomography, X-Ray Computed, business, Software
Abstract: The RAPID© software is the most commonly used computed tomography perfusion (CTP) software in stroke centers. It is estimated that about 1300 hospitals in the world are using this software for decision-making in ischemic stroke. The software provides the estimated volume of infarction and ischemic penumbra, so it is the backbone of treatment planning in these patients. In this manuscript, we present two cases of subacute infarction with misleading CTP using RAPID© software. We believe that given the popularity of this software and increasing application of CTP in subacute infarction, this pitfall is likely underdiagnosed in many patients. In a subacute phase of infarction, we recommend diffusion-weighted imaging magnetic resonance imaging (DWI-MRI) for estimation of infarction to avoid this pitfall and possible mismanagement.
Published: 2019

47. Superior ophthalmic vein thrombosis: What radiologist and clinician must know?

Author: Omid Shafaat, Ehsan Sotoudeh, Houman Sotoudeh, Michael S. Vaphiades, Noha A. Aboueldahab, and Joshua D. Bernstock
Subjects: lcsh:Medical physics. Medical radiology. Nuclear medicine, medicine.medical_specialty, lcsh:R895-920, RA, rheumatoid arthritis, B/L, bilateral, CCF, carotid cavernous fistula, N/A, not applicable, Cavernous sinus thrombosis, SOVT, superior ophthalmic vein thrombosis, Article, 030218 nuclear medicine & medical imaging, SLE, systemic lupus erythematosus, 03 medical and health sciences, 0302 clinical medicine, UTI, Urinary Tract Infection, Medicine, Radiology, Nuclear Medicine and imaging, SOV, superior ophthalmic vein, Sinusitis, CST, cavernous sinus thrombosis, Abscess, Carotid-cavernous fistula, SCC, Squamous Cell Carcinoma, MRI, magnetic resonance Imaging, business.industry, F, female, L, left, R, right, Superior ophthalmic vein thrombosis, Thrombophlebitis, medicine.disease, Thrombosis, CT, computed tomography, Y, yes, Orbital cellulitis, 030220 oncology & carcinogenesis, Cavernous sinus, M, male, Radiology, business, Superior ophthalmic vein, IRB, institutional review board
Abstract: Highlights • Incidental SOVT: good prognosis, self-limited and treated by anticoagulation. • Post-traumatic and surgical SOVT: good prognosis, treated by anticoagulation. • Septic SOVT: secondary to infections, high risk, requires specific interventions. • Aseptic SOVT: secondary to inflammatory diseases, need to treat underlying etiology. • CT and MRI are vital to the correct diagnosis and management of patients with SOVT., Purpose Superior ophthalmic vein thrombosis (SOVT) is an extremely rare condition. Few studies have been published about clinical aspects of this condition. In this study, we have studied the symptoms, underlying etiologies, treatment, pathogenesis and complication of the SOVT and we tried to classify it based on the etiology, treatment, and prognosis. Methods We reviewed the patients’ data from a tertiary academic referral center. Each patient with SOVT was then reviewed for symptoms associated with SOVT, underlying etiology, treatment protocol, treatment response, complications, possible pathogens, and final outcome. Results Twenty-four cases of SOVT were included in this study. Overall, 13 cases were diagnosed as right-sided SOVT, out of which, eight had simultaneous right-sided cavernous sinus thrombosis (CST). Eighteen cases were diagnosed to have left-sided SOVT, out of which, 11 had simultaneous left-sided CST. Conclusions The SOVT can be secondary to different mechanisms. The SOVT secondary to trauma, recent surgery and coagulopathy are mostly non-aggressive, and can be managed by conservative therapy and anticoagulation. The SOVT in patients with orbital cellulitis, history of active sinusitis or paranasal sinus surgery are aggressive presenting with acute orbital swelling, abscess and visual loss. This type of SOVT can be complicated by extension to the cavernous sinus and intracranial structures. These patients require urgent antibiotics therapy and sinus surgery. The most severe type of SOVT is caused by mucormycosis which may also extend intracranially resulting in stroke and is often life-threatening.
Published: 2019

48. Correction to: Cerebellar Microstructural Abnormalities in Parkinson’s Disease: a Systematic Review of Diffusion Tensor Imaging Studies

Author: Maryam Haghshomar, Parnian Shobeiri, Seyed Arsalan Seyedi, Fatemeh Abbasi-Feijani, Amirhossein Poopak, Houman Sotoudeh, Arash Kamali, and Mohammad Hadi Aarabi
Subjects: Neurology, Neurology (clinical)
Published: 2022

49. Luxury perfusion: A paradoxical finding and pitfall of CT perfusion in subacute infarction of brain

Author: Asim K. Bag, Houman Sotoudeh, Omid Shafaat, and Aparna Singhal
Subjects: lcsh:Medical physics. Medical radiology. Nuclear medicine, medicine.medical_specialty, lcsh:R895-920, Ischemia, Infarction, Context (language use), Perfusion scanning, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, Radiology, Nuclear Medicine and imaging, cardiovascular diseases, Stroke, business.industry, Penumbra, medicine.disease, Perfusion imaging, Neurologic examination, Neuroradiology, Laterality, Cardiology, business, Perfusion, 030217 neurology & neurosurgery
Abstract: In the context of recent guidelines for stroke management, application of computed tomographic perfusion (CTP) is anticipated to increase, especially within 6-24 hours after stroke onset. There are commercially available software packages to help to detect the infarct core and the penumbra, however, the results of these software are not always straightforward that might cause incorrect CTP interpretation. We present here a case of acute ischemic stroke in which the CTP software misinterpreted the results by labeling the normal hemisphere as infarction and ischemia due to luxury perfusion of the infarcted hemisphere. Awareness of laterality of the patient's symptoms and the understanding of functioning of perfusion analysis software is necessary to avoid this pitfall. Keywords: Perfusion imaging, Stroke, Neurologic examination
Published: 2018

50. Optic Nerve Infarction in a Patient With Coronavirus Disease 2019

Author: Michael S. Vaphides, Zahra Saadatpour, Houman Sotoudeh, Lanning B. Kline, Ali Rezaei, and Mehdi Tavakoli
Subjects: 2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), business.industry, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Clinical Neurology, COVID-19, Optic Nerve, Virology, Ophthalmology, Infarction, Optic Nerve Diseases, Medicine, Humans, Neurology (clinical), business, Optic nerve infarction
Published: 2021

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