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1. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Haldar, Debanjan, Jiang, Zhifan, Zapaishchykova, Anna, Pavaine, Julija, Shah, Lubdha M., Jones, Blaise V., Sheth, Nakul, Prabhu, Sanjay P., McAllister, Aaron S., Tu, Wenxin, Nandolia, Khanak K., Rodriguez, Andres F., Shaikh, Ibraheem Salman, Montano, Mariana Sanchez, Lai, Hollie Anne, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anderson, Hannah, Anwar, Syed Muhammed, Aristizabal, Alejandro, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Eddy, James, Ezhov, Ivan, Familiar, Ariana M., Farahani, Keyvan, Gandhi, Deep, Gottipati, Anurag, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Elaine, Elaine, Karargyris, Alexandros, Kassem, Hasan, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei B., Maleki, Nazanin, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W., Pati, Sarthak, Piraud, Marie, Poussaint, Tina, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Sheller, MIcah, Shinohara, Russell Takeshi, Viswanathan, Karthik, Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter F., Davatzikos, Christos, Storm, Phillip B., Bornhorst, Miriam, Packer, Roger, Hummel, Trent, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Ware, Jeffrey B., Kann, Benjamin H., Rood, Brian, Resnick, Adam, Bakas, Spyridon, Vossough, Arastoo, and Linguraru, Marius George

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

Published
2024

2. Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

Author

LaBella, Dominic, Baid, Ujjwal, Khanna, Omaditya, McBurney-Lin, Shan, McLean, Ryan, Nedelec, Pierre, Rashid, Arif, Tahon, Nourel Hoda, Altes, Talissa, Bhalerao, Radhika, Dhemesh, Yaseen, Godfrey, Devon, Hilal, Fathi, Floyd, Scott, Janas, Anastasia, Kazerooni, Anahita Fathi, Kirkpatrick, John, Kent, Collin, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anwar, Syed Muhammad, Bergquist, Timothy, Chen, Sully Francis, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Khalili, Nastaran, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Liu, Xinyang, Mahtabfar, Aria, Meier, Zeke, Moawad, Ahmed W., Mongan, John, Piraud, Marie, Shinohara, Russell Takeshi, Wiggins, Walter F., Abayazeed, Aly H., Akinola, Rachel, Jakab, András, Bilello, Michel, de Verdier, Maria Correia, Crivellaro, Priscila, Davatzikos, Christos, Farahani, Keyvan, Freymann, John, Hess, Christopher, Huang, Raymond, Lohmann, Philipp, Moassefi, Mana, Pease, Matthew W., Vollmuth, Phillipp, Sollmann, Nico, Diffley, David, Nandolia, Khanak K., Warren, Daniel I., Hussain, Ali, Fehringer, Pascal, Bronstein, Yulia, Deptula, Lisa, Stein, Evan G., Taherzadeh, Mahsa, de Oliveira, Eduardo Portela, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie M. T., Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David Maximilian, Mansour, Aya, Shawali, Islam H., Yordanov, Nikolay, Stein, Joel M., Hourani, Roula, Moshebah, Mohammed Yahya, Abouelatta, Ahmed Magdy, Rizvi, Tanvir, Willms, Klara, Martin, Dann C., Okar, Abdullah, D'Anna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad Ammar, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Alonso-Basanta, Michelle, Villanueva-Meyer, Javier, Rauschecker, Andreas M., Nada, Ayman, Aboian, Mariam, Flanders, Adam E., Wiestler, Benedikt, Bakas, Spyridon, and Calabrese, Evan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps., Comment: 16 pages, 11 tables, 10 figures, MICCAI

Published
2024

3. The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Gandhi, Deep, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Franson, Andrea, Gottipati, Anurag, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Johansen, Elaine, Jones, Blaise V, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Lai, Hollie Anne, Van Leemput, Koen, Li, Hongwei Bran, Maleki, Nazanin, McAllister, Aaron S, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W, Nandolia, Khanak K, Pavaine, Julija, Piraud, Marie, Poussaint, Tina, Prabhu, Sanjay P, Reitman, Zachary, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Sheth, Nakul, Tu, Wenxin, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Zapaishchykova, Anna, Bornhorst, Miriam, Deutsch, Michelle, Fouladi, Maryam, Lazow, Margot, Mikael, Leonie, Hummel, Trent, Kann, Benjamin, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Packer, Roger, Bakas, Spyridon, Resnick, Adam, Rood, Brian, Vossough, Arastoo, and Linguraru, Marius George

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors., Comment: arXiv admin note: substantial text overlap with arXiv:2305.17033

Published
2024

4. Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

Author

Vossough, Arastoo, Khalili, Nastaran, Familiar, Ariana M., Gandhi, Deep, Viswanathan, Karthik, Tu, Wenxin, Haldar, Debanjan, Bagheri, Sina, Anderson, Hannah, Haldar, Shuvanjan, Storm, Phillip B., Resnick, Adam, Ware, Jeffrey B., Nabavizadeh, Ali, and Kazerooni, Anahita Fathi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics
Abstract

Brain tumors are the most common solid tumors and the leading cause of cancer-related death among children. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high inter-operator variability, underscoring the need for more efficient methods. We compared two deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after training with pediatric-specific multi-institutional brain tumor data using based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of 339 pediatric patients (n=293 internal and n=46 external cohorts) with a variety of tumor subtypes, were preprocessed and manually segmented into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). After training, performance of the two models on internal and external test sets was evaluated using Dice scores, sensitivity, and Hausdorff distance with reference to ground truth manual segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD (median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET, 0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET, 0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were significantly higher for nnU-Net (p<=0.01). External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively. Pediatric-specific data trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

Published
2024

5. A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network

Author

Familiar, Ariana M., Kazerooni, Anahita Fathi, Anderson, Hannah, Lubneuski, Aliaksandr, Viswanathan, Karthik, Breslow, Rocky, Khalili, Nastaran, Bagheri, Sina, Haldar, Debanjan, Kim, Meen Chul, Arif, Sherjeel, Madhogarhia, Rachel, Nguyen, Thinh Q., Frenkel, Elizabeth A., Helili, Zeinab, Harrison, Jessica, Farahani, Keyvan, Linguraru, Marius George, Bagci, Ulas, Velichko, Yury, Stevens, Jeffrey, Leary, Sarah, Lober, Robert M., Campion, Stephani, Smith, Amy A., Morinigo, Denise, Rood, Brian, Diamond, Kimberly, Pollack, Ian F., Williams, Melissa, Vossough, Arastoo, Ware, Jeffrey B., Mueller, Sabine, Storm, Phillip B., Heath, Allison P., Waanders, Angela J., Lilly, Jena V., Mason, Jennifer L., Resnick, Adam C., and Nabavizadeh, Ali

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-na\"ive images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

Published
2023

6. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Haldar, Debanjan, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Johansen, Elaine, Jones, Blaise V, Kofler, Florian, LaBella, Dominic, Lai, Hollie Anne, Van Leemput, Koen, Li, Hongwei Bran, Maleki, Nazanin, McAllister, Aaron S, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W, Nandolia, Khanak K, Pavaine, Julija, Piraud, Marie, Poussaint, Tina, Prabhu, Sanjay P, Reitman, Zachary, Rodriguez, Andres, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Shah, Lubdha M., Sheth, Nakul, Shinohara, Russel Taki, Tu, Wenxin, Viswanathan, Karthik, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Wiggins, Walter, Zapaishchykova, Anna, Aboian, Mariam, Bornhorst, Miriam, de Blank, Peter, Deutsch, Michelle, Fouladi, Maryam, Hoffman, Lindsey, Kann, Benjamin, Lazow, Margot, Mikael, Leonie, Nabavizadeh, Ali, Packer, Roger, Resnick, Adam, Rood, Brian, Vossough, Arastoo, Bakas, Spyridon, and Linguraru, Marius George

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Published
2023

7. Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Author

Fathi Kazerooni, Anahita, Arif, Sherjeel, Madhogarhia, Rachel, Khalili, Nastaran, Haldar, Debanjan, Bagheri, Sina, Familiar, Ariana, Anderson, Hannah, Haldar, Shuvanjan, Tu, Wenxin, Chul Kim, Meen, Viswanathan, Karthik, Muller, Sabine, Prados, Michael, Kline, Cassie, Vidal, Lorenna, Aboian, Mariam, Storm, Phillip, Resnick, Adam, Ware, Jeffrey, Vossough, Arastoo, Davatzikos, Christos, and Nabavizadeh, Ali

Subjects
deep learning, magnetic resonance imaging, pediatric brain tumor, skull-stripping, tumor segmentation
Abstract

BACKGROUND: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. METHODS: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. RESULTS: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. CONCLUSIONS: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Published
2023

10. Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers

Author

Haldar, Debanjan, Kazerooni, Anahita Fathi, Arif, Sherjeel, Familiar, Ariana, Madhogarhia, Rachel, Khalili, Nastaran, Bagheri, Sina, Anderson, Hannah, Shaikh, Ibraheem Salman, Mahtabfar, Aria, Kim, Meen Chul, Tu, Wenxin, Ware, Jefferey, Vossough, Arastoo, Davatzikos, Christos, Storm, Phillip B., Resnick, Adam, and Nabavizadeh, Ali

Published
2023
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11. Towards Consistency in Pediatric Brain Tumor Measurements: Challenges, Solutions, and the Role of AI-Based Segmentation

Author

Familiar, Ariana M, primary, Fathi Kazerooni, Anahita, additional, Vossough, Arastoo, additional, Ware, Jeffrey B, additional, Bagheri, Sina, additional, Khalili, Nastaran, additional, Anderson, Hannah, additional, Haldar, Debanjan, additional, Storm, Phillip B, additional, Resnick, Adam C, additional, Kann, Benjamin H, additional, Aboian, Mariam, additional, Kline, Cassie, additional, Weller, Michael, additional, Huang, Raymond Y, additional, Chang, Susan M, additional, Fangusaro, Jason R, additional, Hoffman, Lindsey M, additional, Mueller, Sabine, additional, Prados, Michael, additional, and Nabavizadeh, Ali, additional

Published
2024
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12. Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

Author

Vossough, Arastoo, primary, Khalili, Nastaran, additional, Familiar, Ariana M., additional, Gandhi, Deep, additional, Viswanathan, Karthik, additional, Tu, Wenxin, additional, Haldar, Debanjan, additional, Bagheri, Sina, additional, Anderson, Hannah, additional, Haldar, Shuvanjan, additional, Storm, Phillip B., additional, Resnick, Adam, additional, Ware, Jeffrey B., additional, Nabavizadeh, Ali, additional, and Fathi Kazerooni, Anahita, additional

Published
2024
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14. The global burden of adolescent and young adult cancer in 2019: a systematic analysis for the Global Burden of Disease Study 2019

Author

Alvarez, Elysia M, Force, Lisa M, Xu, Rixing, Compton, Kelly, Lu, Dan, Henrikson, Hannah Jacqueline, Kocarnik, Jonathan M, Harvey, James D, Pennini, Alyssa, Dean, Frances E, Fu, Weijia, Vargas, Martina T, Keegan, Theresa H M, Ariffin, Hany, Barr, Ronald D, Erdomaeva, Yana Arturovna, Gunasekera, D Sanjeeva, John-Akinola, Yetunde O, Ketterl, Tyler G, Kutluk, Tezer, Malogolowkin, Marcio Henrique, Mathur, Prashant, Radhakrishnan, Venkatraman, Ries, Lynn Ann Gloeckler, Rodriguez-Galindo, Carlos, Sagoyan, Garik Barisovich, Sultan, Iyad, Abbasi, Behzad, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abbastabar, Hedayat, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdoli, Amir, Abebe, Haimanot, Abedi, Aidin, Abidi, Hassan, Abolhassani, Hassan, Abubaker Ali, Hiwa, Abu-Gharbieh, Eman, Achappa, Basavaprabhu, Acuna, Juan Manuel, Adedeji, Isaac Akinkunmi, Adegboye, Oyelola A, Adnani, Qorinah Estiningtyas Sakilah, Advani, Shailesh M, Afzal, Muhammad Sohail, Aghaie Meybodi, Mohamad, Ahadinezhad, Bahman, Ahinkorah, Bright Opoku, Ahmad, Sajjad, Ahmadi, Sepideh, Ahmed, Muktar Beshir, Ahmed Rashid, Tarik, Ahmed Salih, Yusra, Aiman, Wajeeha, Akalu, Gizachew Taddesse, Al Hamad, Hanadi, Alahdab, Fares, AlAmodi, Abdulhadi A, Alanezi, Fahad Mashhour, Alanzi, Turki M, Alem, Adugnaw Zeleke, Alem, Dejene Tsegaye, Alemayehu, Yosef, Alhalaiqa, Fadwa Naji, Alhassan, Robert Kaba, Ali, Saqib, Alicandro, Gianfranco, Alipour, Vahid, Aljunid, Syed Mohamed, Alkhayyat, Motasem, Alluri, Sunitha, Almasri, Nihad A, Al-Maweri, Sadeq Ali, Almustanyir, Sami, Al-Raddadi, Rajaa M, Alvis-Guzman, Nelson, Ameyaw, Edward Kwabena, Amini, Saeed, Amu, Hubert, Ancuceanu, Robert, Andrei, Catalina Liliana, Andrei, Tudorel, Ansari, Fereshteh, Ansari-Moghaddam, Alireza, Anvari, Davood, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Arab-Zozani, Morteza, Argaw, Ayele Mamo, Arshad, Muhammad, Arulappan, Judie, Aryannejad, Armin, Asemi, Zatollah, Asghari Jafarabadi, Mohammad, Atashzar, Mohammad Reza, Atorkey, Prince, Atreya, Alok, Attia, Sameh, Aujayeb, Avinash, Ausloos, Marcel, Avila-Burgos, Leticia, Awedew, Atalel Fentahun, Ayala Quintanilla, Beatriz Paulina, Ayele, Alemu Degu, Ayen, Solomon Shitu, Azab, Mohammed A, Azadnajafabad, Sina, Azami, Hiva, Azangou-Khyavy, Mohammadreza, Azari Jafari, Amirhossein, Azarian, Ghasem, Azzam, Ahmed Y, Bahadory, Saeed, Bai, Jianjun, Baig, Atif Amin, Baker, Jennifer L, Banach, Maciej, Bärnighausen, Till Winfried, Barone-Adesi, Francesco, Barra, Fabio, Barrow, Amadou, Basaleem, Huda, Batiha, Abdul-Monim Mohammad, Behzadifar, Masoud, Bekele, Niguss Cherie, Belete, Rebuma, Belgaumi, Uzma Iqbal, Bell, Arielle Wilder, Berhie, Alemshet Yirga, Bhagat, Devidas S, Bhagavathula, Akshaya Srikanth, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhaskar, Sonu, Bhattacharyya, Krittika, Bhojaraja, Vijayalakshmi S, Bibi, Sadia, Bijani, Ali, Biondi, Antonio, Birara, Setognal, Bjørge, Tone, Bolarinwa, Obasanjo Afolabi, Bolla, Srinivasa Rao, Boloor, Archith, Braithwaite, Dejana, Brenner, Hermann, Bulamu, Norma B, Burkart, Katrin, Bustamante-Teixeira, Maria Teresa, Butt, Nadeem Shafique, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Cao, Chao, Cao, Yin, Carreras, Giulia, Catalá-López, Ferrán, Cembranel, Francieli, Cerin, Ester, Chakinala, Raja Chandra, Chakraborty, Promit Ananyo, Chattu, Vijay Kumar, Chaturvedi, Pankaj, Chaurasia, Akhilanand, Chavan, Prachi P, Chimed-Ochir, Odgerel, Choi, Jee-Young Jasmine, Christopher, Devasahayam J, Chu, Dinh-Toi, Chung, Michael T, Conde, Joao, Costa, Vera Marisa, Da'ar, Omar B, Dadras, Omid, Dahlawi, Saad M A, Dai, Xiaochen, Damiani, Giovanni, D'Amico, Emanuele, Dandona, Lalit, Dandona, Rakhi, Daneshpajouhnejad, Parnaz, Darwish, Amira Hamed, Daryani, Ahmad, De la Hoz, Fernando Pio, Debela, Sisay Abebe, Demie, Takele Gezahegn G, Demissie, Getu Debalkie, Demissie, Zeleke Geto, Denova-Gutiérrez, Edgar, Derbew Molla, Meseret, Desai, Rupak, Desta, Abebaw Alemayehu, Dhamnetiya, Deepak, Dharmaratne, Samath Dhamminda, Dhimal, Mandira Lamichhane, Dhimal, Meghnath, Dianatinasab, Mostafa, Didehdar, Mojtaba, Diress, Mengistie, Djalalinia, Shirin, Do, Huyen Phuc, Doaei, Saeid, Dorostkar, Fariba, dos Santos, Wendel Mombaque, Drake, Thomas M, Ekholuenetale, Michael, El Sayed, Iman, El Sayed Zaki, Maysaa, El Tantawi, Maha, El-Abid, Hassan, Elbahnasawy, Mostafa Ahmed, Elbarazi, Iffat, Elhabashy, Hala Rashad, Elhadi, Muhammed, El-Jaafary, Shaimaa I, Enyew, Daniel Berhanie, Erkhembayar, Ryenchindorj, Eshrati, Babak, Eskandarieh, Sharareh, Faisaluddin, Mohammed, Fares, Jawad, Farooque, Umar, Fasanmi, Abidemi Omolara, Fatima, Wafa, Ferreira de Oliveira, José Miguel P, Ferrero, Simone, Ferro Desideri, Lorenzo, Fetensa, Getahun, Filip, Irina, Fischer, Florian, Fisher, James L, Foroutan, Masoud, Fukumoto, Takeshi, Gaal, Peter Andras, Gad, Mohamed M, Gaewkhiew, Piyada, Gallus, Silvano, Garg, Tushar, Gebremeskel, Teferi Gebru, Gemeda, Belete Negese Belete, Getachew, Tamiru, Ghafourifard, Mansour, Ghamari, Seyyed-Hadi, Ghashghaee, Ahmad, Ghassemi, Fariba, Ghith, Nermin, Gholami, Ali, Gholizadeh Navashenaq, Jamshid, Gilani, Syed Amir, Ginindza, Themba G, Gizaw, Abraham Tamirat, Glasbey, James C, Goel, Amit, Golechha, Mahaveer, Goleij, Pouya, Golinelli, Davide, Gopalani, Sameer Vali, Gorini, Giuseppe, Goudarzi, Houman, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Gubari, Mohammed Ibrahim Mohialdeen, Guerra, Maximiliano Ribeiro, Guha, Avirup, Gupta, Bhawna, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vivek Kumar, Haddadi, Rasool, Hafezi-Nejad, Nima, Hailu, Alemayehu, Haj-Mirzaian, Arvin, Halwani, Rabih, Hamadeh, Randah R, Hambisa, Mitiku Teshome, Hameed, Sajid, Hamidi, Samer, Haque, Shafiul, Hariri, Sanam, Haro, Josep Maria, Hasaballah, Ahmed I, Hasan, S M Mahmudul, Hashemi, Seyedeh Melika, Hassan, Treska S, Hassanipour, Soheil, Hay, Simon I, Hayat, Khezar, Hebo, Sultan H, Heidari, Golnaz, Heidari, Mohammad, Herrera-Serna, Brenda Yuliana, Herteliu, Claudiu, Heyi, Demisu Zenbaba, Hezam, Kamal, Hole, Michael K, Holla, Ramesh, Horita, Nobuyuki, Hossain, Md Mahbub, Hossain, Mohammad Bellal, Hosseini, Mohammad-Salar, Hosseini, Mostafa, Hosseinzadeh, Ali, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Househ, Mowafa, Hsairi, Mohamed, Huang, Junjie, Hussein, Nawfal R, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Ilic, Milena D, Innos, Kaire, Irham, Lalu Muhammad, Islam, Rakibul M, Islam, Sheikh Mohammed Shariful, Ismail, Nahlah Elkudssiah, Isola, Gaetano, Iwagami, Masao, Jacob, Louis, Jadidi-Niaragh, Farhad, Jain, Vardhmaan, Jakovljevic, Mihajlo, Janghorban, Roksana, Javadi Mamaghani, Amirreza, Jayaram, Shubha, Jayawardena, Ranil, Jazayeri, Seyed Behzad, Jebai, Rime, Jha, Ravi Prakash, Joo, Tamas, Joseph, Nitin, Joukar, Farahnaz, Jürisson, Mikk, Kaambwa, Billingsley, Kabir, Ali, Kalankesh, Leila R, Kaliyadan, Feroze, Kamal, Zul, Kamath, Ashwin, Kandel, Himal, Kar, Sitanshu Sekhar, Karaye, Ibraheem M, Karimi, Amirali, Kassa, Bekalu Getnet, Kauppila, Joonas H, Kemp Bohan, Phillip M, Kengne, Andre Pascal, Kerbo, Amene Abebe, Keykhaei, Mohammad, Khader, Yousef Saleh, Khajuria, Himanshu, Khalili, Nastaran, Khalili, Neda, Khan, Ejaz Ahmad, Khan, Gulfaraz, Khan, Maseer, Khan, Md Nuruzzaman, Khan, Moien AB, Khanali, Javad, Khayamzadeh, Maryam, Khosravizadeh, Omid, Khubchandani, Jagdish, Khundkar, Roba, Kim, Min Seo, Kim, Yun Jin, Kisa, Adnan, Kisa, Sezer, Kissimova-Skarbek, Katarzyna, Kolahi, Ali-Asghar, Kopec, Jacek A, Koteeswaran, Rajasekaran, Koulmane Laxminarayana, Sindhura Lakshmi, Koyanagi, Ai, Kugbey, Nuworza, Kumar, G Anil, Kumar, Nithin, Kwarteng, Alexander, La Vecchia, Carlo, Lan, Qing, Landires, Iván, Lasrado, Savita, Lauriola, Paolo, Ledda, Caterina, Lee, Sang-woong, Lee, Wei-Chen, Lee, Yeong Yeh, Lee, Yo Han, Leigh, James, Leong, Elvynna, Li, Bingyu, Li, Jiarui, Li, Ming-Chieh, Lim, Stephen S, Liu, Xuefeng, Lobo, Stany W, Loureiro, Joana A, Lugo, Alessandra, Lunevicius, Raimundas, Magdy Abd El Razek, Hassan, Magdy Abd El Razek, Muhammed, Mahmoudi, Morteza, Majeed, Azeem, Makki, Alaa, Male, Shilpa, Malekpour, Mohammad-Reza, Malekzadeh, Reza, Malik, Ahmad Azam, Mamun, Mohammed A, Manafi, Navid, Mansour-Ghanaei, Fariborz, Mansouri, Borhan, Mansournia, Mohammad Ali, Martini, Santi, Masoumi, Seyedeh Zahra, Matei, Clara N, Mathur, Manu Raj, McAlinden, Colm, Mehrotra, Ravi, Mendoza, Walter, Menezes, Ritesh G, Mentis, Alexios-Fotios A, Meretoja, Tuomo J, Mersha, Amanual Getnet, Mesregah, Mohamed Kamal, Mestrovic, Tomislav, Miao Jonasson, Junmei, Miazgowski, Bartosz, Michalek, Irmina Maria, Miller, Ted R, Mingude, Alemu Basazin, Mirmoeeni, Seyyedmohammadsadeq, Mirzaei, Hamed, Misra, Sanjeev, Mithra, Prasanna, Mohammad, Karzan Abdulmuhsin, Mohammadi, Mokhtar, Mohammadi, Seyyede Momeneh, Mohammadian-Hafshejani, Abdollah, Mohammadpourhodki, Reza, Mohammed, Arif, Mohammed, Shafiu, Mohammed, Teroj Abdulrahman, Moka, Nagabhishek, Mokdad, Ali H, Molokhia, Mariam, Momtazmanesh, Sara, Monasta, Lorenzo, Moni, Mohammad Ali, Moradi, Ghobad, Moradi, Yousef, Moradzadeh, Maliheh, Moradzadeh, Rahmatollah, Moraga, Paula, Morrison, Shane Douglas, Mostafavi, Ebrahim, Mousavi Khaneghah, Amin, Mpundu-Kaambwa, Christine, Mubarik, Sumaira, Mwanri, Lillian, Nabhan, Ashraf F, Nagaraju, Shankar Prasad, Nagata, Chie, Naghavi, Mohsen, Naimzada, Mukhammad David, Naldi, Luigi, Nangia, Vinay, Naqvi, Atta Abbas, Narasimha Swamy, Sreenivas, Narayana, Aparna Ichalangod, Nayak, Biswa Prakash, Nayak, Vinod C, Nazari, Javad, Nduaguba, Sabina Onyinye, Negoi, Ionut, Negru, Serban Mircea, Nejadghaderi, Seyed Aria, Nepal, Samata, Neupane Kandel, Sandhya, Nggada, Haruna Asura, Nguyen, Cuong Tat, Nnaji, Chukwudi A, Nosrati, Hamed, Nouraei, Hasti, Nowroozi, Ali, Nuñez-Samudio, Virginia, Nwatah, Vincent Ebuka, Nzoputam, Chimezie Igwegbe, Oancea, Bogdan, Odukoya, Oluwakemi Ololade, Oguntade, Ayodipupo Sikiru, Oh, In-Hwan, Olagunju, Andrew T, Olagunju, Tinuke O, Olakunde, Babayemi Oluwaseun, Oluwasanu, Mojisola Morenike, Omar, Emad, Omar Bali, Ahmed, Ong, Sokking, Onwujekwe, Obinna E, Ortega-Altamirano, Doris V, Otstavnov, Nikita, Otstavnov, Stanislav S, Oumer, Bilcha, Owolabi, Mayowa O, P A, Mahesh, Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Pakshir, Keyvan, Pana, Adrian, Pandey, Anamika, Pardhan, Shahina, Pashazadeh Kan, Fatemeh, Pasovic, Maja, Patel, Jenil R, Pati, Siddhartha, Pattanshetty, Sanjay M, Paudel, Uttam, Pereira, Renato B, Peres, Mario F P, Perianayagam, Arokiasamy, Postma, Maarten J, Pourjafar, Hadi, Pourshams, Akram, Prashant, Akila, Pulakunta, Thejodhar, Qadir, Mirza Muhammad Fahd Fahd, Rabiee, Mohammad, Rabiee, Navid, Radfar, Amir, Radhakrishnan, Raghu Anekal, Rafiee, Ata, Rafiei, Alireza, Rafiei, Sima, Rahim, Fakher, Rahimzadeh, Shadi, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rajesh, Aashish, Ramezani-Doroh, Vajiheh, Ranabhat, Kamal, Ranasinghe, Priyanga, Rao, Chythra R, Rao, Sowmya J, Rashedi, Sina, Rashidi, Mahsa, Rashidi, Mohammad-Mahdi, Rath, Goura Kishor, Rawaf, David Laith, Rawaf, Salman, Rawal, Lal, Rawassizadeh, Reza, Razeghinia, Mohammad Sadegh, Regasa, Misganu Teshoma, Renzaho, Andre M N, Rezaei, Maryam, Rezaei, Negar, Rezaei, Nima, Rezaeian, Mohsen, Rezapour, Aziz, Rezazadeh-Khadem, Sahba, Riad, Abanoub, Rios Lopez, Ligia Estefania, Rodriguez, Jefferson Antonio Buendia, Ronfani, Luca, Roshandel, Gholamreza, Rwegerera, Godfrey M, Saber-Ayad, Maha Mohamed, Sabour, Siamak, Saddik, Basema, Sadeghi, Erfan, Sadeghian, Saeid, Saeed, Umar, Sahebkar, Amirhossein, Saif-Ur-Rahman, KM, Sajadi, S Mohammad, Salahi, Sarvenaz, Salehi, Sana, Salem, Marwa Rashad, Salimzadeh, Hamideh, Samy, Abdallah M, Sanabria, Juan, Sanmarchi, Francesco, Sarveazad, Arash, Sathian, Brijesh, Sawhney, Monika, Sawyer, Susan M, Saylan, Mete, Schneider, Ione Jayce Ceola, Seidu, Abdul-Aziz, Šekerija, Mario, Sendo, Endalew Gemechu, Sepanlou, Sadaf G, Seylani, Allen, Seyoum, Kenbon, Sha, Feng, Shafaat, Omid, Shaikh, Masood Ali, Shamsoddin, Erfan, Shannawaz, Mohammed, Sharma, Rajesh, Sheikhbahaei, Sara, Shetty, Adithi, Shetty, B Suresh Kumar, Shetty, Pavanchand H, Shin, Jae Il, Shirkoohi, Reza, Shivakumar, K M, Shobeiri, Parnian, Siabani, Soraya, Sibhat, Migbar Mekonnen, Siddappa Malleshappa, Sudeep K, Sidemo, Negussie Boti, Silva, Diego Augusto Santos, Silva Julian, Guilherme, Singh, Achintya Dinesh, Singh, Jasvinder A, Singh, Jitendra Kumar, Singh, Surjit, Sinke, Abiy H, Sintayehu, Yitagesu, Skryabin, Valentin Yurievich, Skryabina, Anna Aleksandrovna, Smith, Lee, Sofi-Mahmudi, Ahmad, Soltani-Zangbar, Mohammad Sadegh, Song, Suhang, Spurlock, Emma Elizabeth, Steiropoulos, Paschalis, Straif, Kurt, Subedi, Ranjeeta, Sufiyan, Mu'awiyyah Babale, Suliankatchi Abdulkader, Rizwan, Sultana, Saima, Szerencsés, Viktória, Szócska, Miklós, Tabaeian, Seidamir Pasha, Tabarés-Seisdedos, Rafael, Tabary, Mohammadreza, Tabuchi, Takahiro, Tadbiri, Hooman, Taheri, Majid, Taherkhani, Amir, Takahashi, Ken, Tampa, Mircea, Tan, Ker-Kan, Tat, Vivian Y, Tavakoli, Ahmad, Tbakhi, Abdelghani, Tehrani-Banihashemi, Arash, Temsah, Mohamad-Hani, Tesfay, Fisaha Haile, Tesfaye, Bekele, Thakur, Jarnail Singh, Thapar, Rekha, Thavamani, Aravind, Thiyagarajan, Arulmani, Thomas, Nihal, Tobe-Gai, Ruoyan, Togtmol, Munkhsaikhan, Tohidast, Seyed Abolfazl, Tohidinik, Hamid Reza, Tolani, Musliu Adetola, Tollosa, Daniel Nigusse, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Traini, Eugenio, Tran, Bach Xuan, Tran, Mai Thi Ngoc, Tripathy, Jaya Prasad, Tusa, Biruk Shalmeno, Ukke, Gebresilasea Gendisha, Ullah, Irfan, Ullah, Saif, Umapathi, Krishna Kishore, Unnikrishnan, Bhaskaran, Upadhyay, Era, Ushula, Tolassa Wakayo, Vacante, Marco, Valadan Tahbaz, Sahel, Varthya, Shoban Babu, Veroux, Massimiliano, Villeneuve, Paul J, Violante, Francesco S, Vlassov, Vasily, Vu, Giang Thu, Waheed, Yasir, Wang, Ning, Ward, Paul, Weldesenbet, Adisu Birhanu, Wen, Yi Feng, Westerman, Ronny, Winkler, Andrea Sylvia, Wubishet, Befikadu Legesse, Xu, Suowen, Yahyazadeh Jabbari, Seyed Hossein, Yang, Lin, Yaya, Sanni, Yazdi-Feyzabadi, Vahid, Yazie, Taklo Simeneh, Yehualashet, Sisay Shewasinad, Yeshaneh, Alex, Yeshaw, Yigizie, Yirdaw, Birhanu Wubale, Yonemoto, Naohiro, Younis, Mustafa Z, Yousefi, Zabihollah, Yu, Chuanhua, Yunusa, Ismaeel, Zadnik, Vesna, Zahir, Mazyar, Zahirian Moghadam, Telma, Zamani, Mohammad, Zamanian, Maryam, Zandian, Hamed, Zare, Fariba, Zastrozhin, Mikhail Sergeevich, Zastrozhina, Anasthasia, Zhang, Jianrong, Zhang, Zhi-Jiang, Ziapour, Arash, Zoladl, Mohammad, Murray, Christopher J L, Fitzmaurice, Christina, Bleyer, Archie, and Bhakta, Nickhill

Published
2022
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15. Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.

Author

Familiar, Ariana M, Kazerooni, Anahita Fathi, Vossough, Arastoo, Ware, Jeffrey B, Bagheri, Sina, Khalili, Nastaran, Anderson, Hannah, Haldar, Debanjan, Storm, Phillip B, Resnick, Adam C, Kann, Benjamin H, Aboian, Mariam, Kline, Cassie, Weller, Michael, Huang, Raymond Y, Chang, Susan M, Fangusaro, Jason R, Hoffman, Lindsey M, Mueller, Sabine, and Prados, Michael

Published
2024
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16. Statistical plots in oncologic imaging, a primer for neuroradiologists.

Author

Bagheri, Sina, Taghvaei, Mohammad, Familiar, Ariana, Haldar, Debanjan, Zandifar, Alireza, Khalili, Nastaran, Vossough, Arastoo, and Nabavizadeh, Ali

Abstract

The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Correlation Between Low-Dose Chest Computed Tomography and RT-PCR Results for the Diagnosis of COVID-19: A Report of 27,824 Cases in Tehran, Iran

Author

Zali, Alireza, Sohrabi, Mohammad-Reza, Mahdavi, Ali, Khalili, Nastaran, Taheri, Morteza Sanei, Maher, Ali, Sadoughi, Mohammadmehdi, Zarghi, Afshin, Ziai, Seyed Ali, Shabestari, Abbas Arjmand, Bakhshayeshkaram, Mehrdad, Haghighatkhah, Hamidreza, Salevatipour, Babak, Abrishami, Alireza, Raoufi, Masoomeh, Dehghan, Pooneh, Bagheri, Arash Khameneh, Khoshnoud, Reza Jalili, and Hanani, Khatereh

Published
2021
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21. NIMG-42. RADIOIMMUNOMIC SIGNATURES IN PEDIATRIC LOW-GRADE GLIOMA FOR NON-INVASIVE IDENTIFICATION OF IMMUNOTHERAPEUTIC TARGETS

Author

Kazerooni, Anahita Fathi, primary, Kraya, Adam, additional, Rathi, Komal, additional, Khalili, Nastaran, additional, Haldar, Debanjan, additional, Familiar, Ariana, additional, Kim, Meen Chul, additional, Bagheri, Sina, additional, Anderson, Hannah, additional, Foster, Jessica, additional, Storm, Phillip, additional, Davatzikos, Christos, additional, Ware, Jeffrey B, additional, Vossough, Arastoo, additional, Resnick, Adam, additional, and Nabavizadeh, Ali, additional

Published
2023
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22. NIMG-87. PEDIATRIC BRAIN TUMOR SEGMENTATION INITIATIVE AT CHILDREN’S BRAIN TUMOR NETWORK (CBTN); PROJECT UPDATE ON IMPROVED PERFORMANCE AND FUTURE PROSPECTS

Author

Kazerooni, Anahita Fathi, primary, Khalili, Nastaran, additional, Haldar, Debanjan, additional, Viswanathan, Karthik, additional, Familiar, Ariana, additional, Bagheri, Sina, additional, Anderson, Hannah, additional, Mahtabfar, Aria, additional, Storm, Phillip, additional, Davatzikos, Christos, additional, Resnick, Adam, additional, Ware, Jeffrey B, additional, Vossough, Arastoo, additional, and Nabavizadeh, Ali, additional

Published
2023
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24. NIMG-32. MULTI-MODAL ANALYSIS UNCOVERS DIFFERENTIALLY REGULATED PATHWAYS ASSOCIATED WITH RADIO-PHENOTYPES IN PEDIATRIC LOW-GRADE GLIOMA

Author

Kazerooni, Anahita Fathi, primary, Kraya, Adam, additional, Rathi, Komal, additional, Haldar, Debanjan, additional, Mahtabfar, Aria, additional, Khalili, Nastaran, additional, Familiar, Ariana, additional, Kim, Meen Chul, additional, Bagheri, Sina, additional, Anderson, Hannah, additional, Ware, Jeffrey B, additional, Storm, Phillip, additional, Davatzikos, Christos, additional, Vossough, Arastoo, additional, Resnick, Adam, additional, and Nabavizadeh, Ali, additional

Published
2023
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31. Adding Value to Liquid Biopsy for Brain Tumors: The Role of Imaging.

Author

Khalili, Nastaran, Shooli, Hossein, Hosseini, Nastaran, Fathi Kazerooni, Anahita, Familiar, Ariana, Bagheri, Sina, Anderson, Hannah, Bagley, Stephen J., and Nabavizadeh, Ali

Subjects
*PROFESSIONAL practice, *NEUROLOGY, *BLOOD-brain barrier, *DNA, *EARLY detection of cancer, *MAGNETIC resonance imaging, *POSITRON emission tomography computed tomography, *BRAIN tumors, *TUMOR markers, *CANCER patient medical care, BRAIN tumor diagnosis, CENTRAL nervous system tumors, BODY fluid examination
Abstract

Simple Summary: Clinical management in neuro-oncology has shifted to an integrated method that combines molecular profiles with histopathological and imaging data. Liquid biopsy is a non-invasive method that captures the molecular diversity of the whole tumor by detecting specific tumor biomarkers that circulate in body fluids like the cerebrospinal fluid. However, the limited presence and short half-life of tumor-derived biomarkers, especially in central nervous system (CNS) tumors, have restricted the use of liquid biopsy in clinical settings. Here, we review the diverse clinical applications of liquid biopsy in CNS tumors and discuss the added value of imaging in enhancing the release and detection of circulating tumor biomarkers. Clinical management in neuro-oncology has changed to an integrative approach that incorporates molecular profiles alongside histopathology and imaging findings. While the World Health Organization (WHO) guideline recommends the genotyping of informative alterations as a routine clinical practice for central nervous system (CNS) tumors, the acquisition of tumor tissue in the CNS is invasive and not always possible. Liquid biopsy is a non-invasive approach that provides the opportunity to capture the complex molecular heterogeneity of the whole tumor through the detection of circulating tumor biomarkers in body fluids, such as blood or cerebrospinal fluid (CSF). Despite all of the advantages, the low abundance of tumor-derived biomarkers, particularly in CNS tumors, as well as their short half-life has limited the application of liquid biopsy in clinical practice. Thus, it is crucial to identify the factors associated with the presence of these biomarkers and explore possible strategies that can increase the shedding of these tumoral components into biological fluids. In this review, we first describe the clinical applications of liquid biopsy in CNS tumors, including its roles in the early detection of recurrence and monitoring of treatment response. We then discuss the utilization of imaging in identifying the factors that affect the detection of circulating biomarkers as well as how image-guided interventions such as focused ultrasound can help enhance the presence of tumor biomarkers through blood–brain barrier (BBB) disruption. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Prognostic Value of Clinical and Computed Tomography Score in Predicting Outcome of Patients with COVID-19: A prospective study in Iran

Author

Mahdavi, Arash, primary, Khalili, Nastaran, additional, Dehkordi, Saeid Alerasoul, additional, Tajbakhsh, Ardeshir, additional, Davarpanah, Amir H., additional, Mahdavi, Ali, additional, Zolghadr, Zahra, additional, Langroudi, Taraneh Faghihi, additional, Taheri, Morteza Sanei, additional, and Shabestari, Abbas Arjmand, additional

Published
2023
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33. Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study

Author

Kazerooni, Anahita Fathi, primary, Arif, Sherjeel, additional, Madhogarhia, Rachel, additional, Khalili, Nastaran, additional, Haldar, Debanjan, additional, Bagheri, Sina, additional, Familiar, Ariana M., additional, Anderson, Hannah, additional, Haldar, Shuvanjan, additional, Tu, Wenxin, additional, Kim, Meen Chul, additional, Viswanathan, Karthik, additional, Muller, Sabine, additional, Prados, Michael, additional, Kline, Cassie, additional, Vidal, Lorenna, additional, Aboian, Mariam, additional, Storm, Phillip B., additional, Resnick, Adam C., additional, Ware, Jeffrey B., additional, Vossough, Arastoo, additional, Davatzikos, Christos, additional, and Nabavizadeh, Ali, additional

Published
2023
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34. NIMG-87. CHARACTERIZING IMMUNE PROFILES OF PEDIATRIC MEDULLOBLASTOMA AND THEIR RADIOLOGICAL CORRELATES

Author

Familiar, Ariana, primary, Zhao, Chao, additional, Kim, Meen Chul, additional, Khalili, Nastaran, additional, Madhogarhia, Rachel, additional, Arif, Sherjeel, additional, Bagheri, Sina, additional, Anderson, Hannah, additional, Haldar, Debanjan, additional, Ware, Jeffrey B, additional, Vossough, Arastoo, additional, Storm, Philip B, additional, Resnick, Adam C, additional, Kazerooni, Anahita Fathi, additional, and Nabavizadeh, Ali, additional

Published
2022
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35. NIMG-74. RADIOIMMUNOMIC SIGNATURES IN PEDIATRIC LOW-GRADE GLIOMA BASED ON MULTIPARAMETRIC MRI SCANS

Author

Kazerooni, Anahita Fathi, primary, Kraya, Adam A, additional, Kim, Meen Chul, additional, Khalili, Nastaran, additional, Arif, Sherjeel, additional, Jin, Run, additional, Rathi, Komal, additional, Familiar, Ariana, additional, Madhogarhia, Rachel, additional, Haldar, Debanjan, additional, Bagheri, Sina, additional, Anderson, Hannah, additional, Shaikh, Ibraheem Salman, additional, Haldar, Shuvanjan, additional, Ware, Jeffrey B, additional, Vossough, Arastoo, additional, Storm, Philip B, additional, Resnick, Adam C, additional, Davatzikos, Christos, additional, and Nabavizadeh, Ali, additional

Published
2022
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38. Hydatid Disease: A Pictorial Review of Uncommon Locations.

Author

Khalili, Nastaran, Iranpour, Pooya, Khalili, Neda, and Haseli, Sara

Subjects
*LIVER, *LUNGS, *TAPEWORMS, *MAGNETIC resonance imaging, *DIAGNOSTIC imaging, *COMPUTED tomography, *HEPATIC echinococcosis, *EARLY diagnosis, *DISEASE management
Abstract

Hydatid disease is a zoonotic infection caused primarily by the tapeworm parasite, Echinococcus granulosus. It is considered an endemic disease in the Mediterranean region. In about 90% of cases, hydatid cysts are found in the liver and lungs; however, any other organ in the body may be affected, particularly in endemic areas. When encountering cystic lesions in these areas, the physician should always keep hydatid disease as a possible diagnosis in mind. To avoid life-threatening conditions such as anaphylactic shock or pressure effect on vital organs, timely diagnosis, and proper management are critical. When a rare site is involved, hydatid disease should be diagnosed using a combination of serologic assays and imaging modalities such as ultrasonography, computed tomography (CT), and magnetic resonance imaging (MRI). These imaging modalities can also be used to determine the extent of the disease and assess possible complications. Here, we present a pictorial review of typical imaging manifestations of hydatid cysts in unusual sites. Being aware of these imaging features will assist physicians in making an accurate, timely diagnosis and subsequently, providing optimal management. [ABSTRACT FROM AUTHOR]

Published
2023
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39. Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Author

Kazerooni, Anahita Fathi, Arif, Sherjeel, Madhogarhia, Rachel, Khalili, Nastaran, Haldar, Debanjan, Bagheri, Sina, Familiar, Ariana M, Anderson, Hannah, Haldar, Shuvanjan, Tu, Wenxin, Kim, Meen Chul, Viswanathan, Karthik, Muller, Sabine, Prados, Michael, Kline, Cassie, Vidal, Lorenna, Aboian, Mariam, Storm, Phillip B, Resnick, Adam C, and Ware, Jeffrey B

Published
2023
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43. Analysis of Information Diffusion through Social Media

Author

Khalili, Nastaran, Industrial and Systems Engineering, Ghaffarzadegan, Navid, Andalib, Maryam Alsadat, Kong, Zhenyu, and Jardine, Eric

Subjects
Information, Communication, Misinformation, Data Analytics, Social Media, System Dynamics, Natural Language Processing
Abstract

The changes in the course of communication changed the world from different perspectives. Public participation on social media means the generation, diffusion, and exposure to a tremendous amount of user-generated content without supervision. This four-essay dissertation analyzes information diffusion through social media and its opportunities and challenges through management systems engineering and data analytics. First, we evaluate how information can be shared to reach maximum exposure for the case on online petitions. We use system dynamics modeling and propose policies for campaign managers to schedule the reminders they send to have the highest number of petition signatures. We find that sending reminders is more effective in the case of increasing the signature rate. In the second essay, we investigate how people build trust/ mistrust in science during an emergency. We use data analytics methods on more than 700,000 tweets containing keywords of Hydroxychloroquine and chloroquine, two candidate medicines, to prevent and cure patients infected with COVID-19. We show that people's opinions are concentrated in the case of polarity and spread out in the case of subjectivity. Also, they tend to share subjective tweets than objective ones. In the third essay, building on the same dataset as essay two, we study the changes in science communication during the coronavirus pandemic. We used topic modeling and clustered the tweets into seven different groups. Our analysis suggests that a highly scientific and health-related subject can become political in the case of an emergency. We found that the groups of medical information and research and study have fewer tweets than the political one. Fourth, we investigated fake news diffusion as one of the main challenges of user-generated content. We built a system dynamics model and analyzed the effects of competition and correction in combating fake news. We show that correction of misinformation and competition in fake news needs a high percentage of participation to be effective enough to deal with fake news. Doctor of Philosophy The prevalence of social media among people has changed information diffusion in several ways. This change caused the emergence of a variety of opportunities and challenges. We discuss instances of these in this dissertation in four main essays. In the first essay, we study online social and political campaigns. Considering the main goal of campaign managers is to gain the highest reach and signatures, we generate a model to show the effects of sending reminders after the initial announcement and its schedule on the final total number of signatures. We found that the best policy for online petition success is sending reminders when people are increasingly signing it rather than when people lose interest in it. In the second essay, we investigated how people build trust/ mistrust in scientific information in emergency cases. We used public tweets about two candidate medicines to prevent and treat patients infected with COVID-19 and analyzed them. Our results suggest that people trust and retweet the information that is based on emotions and judgments more than the one containing facts. We evaluated the science communication during the mentioned emergency by further investigating the same dataset in the third essay. We clustered all the tweets based on the words they used into seven different groups and labeled each of them. Then, we focused on three groups of medical, research and study, and political. Our analysis suggests that although the subject is a health-related scientific one, the number of tweets in the political group is greater than the other clusters. In the fourth essay, we analyzed the fake news diffusion through social media and the effects of correction and competition on it. In this context, correction means the reaction to misinformation that states its falsity or provides counter facts based on truth. We created a model and simulated it for the competition considering novelty as one influential factor of sharing. The results of this study reveal that active participation in correction and competition is needed to combat fake news effectively.

Published
2021

47. Patterns of Antiepileptic Drug Reactions in Children: A Multicenter Study.

Author

Sedighi, Parinaz, Khalili, Neda, Khalili, Nastaran, Doosti-Irani, Amin, Moradi, Atefeh, Moghadam, Samira, Nemati, Meshkat, Jorjafki, Sobhan Mohammadi, Badv, Reza Shervin, and Sedighi, Iraj

Subjects
ANTICONVULSANTS, RESEARCH, RESEARCH methodology, RETROSPECTIVE studies, ACQUISITION of data, ANTIHISTAMINES, EXANTHEMA, MEDICAL records, DESCRIPTIVE statistics, DRUG side effects, PHENOBARBITAL, CHILDREN
Abstract

Objectives Antiepileptic drugs are among the most common triggers of cutaneous adverse reactions. About 5-17% of epileptic patients develop idiosyncratic skin reactions at some point during their treatment course, most of which occur within the first two months of drug initiation. This study aimed to investigate the pattern of cutaneous drug reactions associated with anticonvulsant use among the pediatric population in Iran to identify high-risk individuals. Materials & Methods In this retrospective descriptive study, medical records of children aged two months to 14 years, who were diagnosed with drug reactions due to anticonvulsant drugs between April 2007 and March 2018, were reviewed, and relevant information were extracted. This multicenter study was conducted in several provinces of Iran. Results A total of 186 cases with a final diagnosis of the antiepileptic druginduced eruption were evaluated. The median age of participants was 36 months (range: 2-168), and 56% were male. In approximately 70% of the children, phenobarbital was the culprit. The median time interval between initiation of the causative drug and development of rash and fever was 10 and 7 days, respectively. The most common rash type was maculopapular rash (69%). Overall, 33% of the patients only received antihistamines after discontinuation of the causative drug. Conclusion Similar to previously published studies in Iran, phenobarbital was the main cause of cutaneous drug reactions to antiepileptic drugs, indicating the necessity of paying more attention when prescribing phenobarbital for Iranian pediatrics. [ABSTRACT FROM AUTHOR]

Published
2022
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48. The interplay between aryl hydrocarbon receptor, H. pylori, tryptophan, and arginine in the pathogenesis of gastric cancer.

Author

Pirzadeh, Marzieh, Khalili, Nastaran, and Rezaei, Nima

Subjects
*ARYL hydrocarbon receptors, *HELICOBACTER pylori, *STOMACH cancer, *TRYPTOPHAN, *CARCINOGENESIS, *HELICOBACTER pylori infections
Abstract

Several risk factors are known to be involved in the initiation and development of gastric cancer. Among them, H. pylori is one of the most prominent with multiple virulence factors contributing to its pathogenicity. In this study, we have discussed an interesting immunological cycle exploring the interplay between H. pylori, aryl hydrocarbon receptor (AHR), tryptophan, arginine, and the metabolites of these two amino acids in the development of gastric cancer. AHR is a ligand-activated transcription factor which acts as a regulator for a diverse set of genes and has various types of exogenous and endogenous ligands. The tryptophan metabolite, kynurenine, is one of these ligands that can interact with AHR, leading to immune suppression and subsequently, susceptibility to gastric cancer. On the other hand, H. pylori downregulates the expression of AHR and AHR repressor (AHRR), leading to increased inflammatory cytokine production. A metabolite of the kynurenine pathway, xanthurenic acid, is a potent inhibitor of a terminal enzyme in the synthetic pathway of tetrahydrobiopterin (BH4). BH4, itself, is a cofactor in the process of nitric oxide (NO) production from arginine that has been shown to have immune-enhancing properties. Arginine has also been evidenced to have anti-tumoral function through inducing apoptosis in gastric cell lines; however, controversy exists regarding the anti-tumor role of arginine and BH4, since they are also associated with increased NO production, subsequently promoting tumor angiogenesis. Hence, although several synergistic connections result in immunity improvement, these correlations can also act as a double-edged sword, promoting tumor development. This emphasizes on the need for further investigations to better understand this complex interplay. [ABSTRACT FROM AUTHOR]

Published
2022
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