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Entrapment neuropathies at the elbow are common in clinical practice and require an accurate diagnosis for effective management. Understanding the imaging characteristics of these conditions is essential for confirming diagnoses and identifying underlying causes. Ultrasound serves as the primary imaging modality for evaluating nerve structure and movement, while MRI is superior for detecting muscle denervation. Plain radiography and CT play a minor role and can be used for the evaluation of bony structures and calcifications/ossifications. Comprehensive knowledge of anatomical landmarks, nerve pathways, and compression sites is crucial for clinicians to accurately interpret imaging and guide appropriate treatment strategies for entrapments of ulnar, median, and radial nerves, and their branches. Critical relevance statement: Accurate imaging and anatomical knowledge are essential for diagnosing elbow entrapment neuropathies. Ultrasound is the preferred modality for assessing nerve structure and motion, while MRI excels in detecting muscle denervation and guiding effective management of ulnar, median, and radial nerve entrapments. Key Points: Ultrasound is the primary modality for assessing nerve structure and stability. Findings include nerve structural loss, isoechogenicity, thickening, and hyper-vascularization. MRI provides a comprehensive evaluation of the elbow and accurate muscle assessment. Imaging allows the identification of compressive causes, including anatomical variants, masses, or osseous anomalies. Awareness of anatomical landmarks, nerve pathways, and compression sites is essential. [ABSTRACT FROM AUTHOR]

Background People with cystic fibrosis (CF) can experience recurrent chest infections, pancreatic exocrine insufficiency and gastrointestinal symptoms. New cystic fibrosis transmembrane conductance regulator (CFTR) modulator drugs improve lung function but gastrointestinal effects are unclear. We aimed to see if a CFTR modulator (tezacaftor-ivacaftor,TEZ/IVA) improves gastrointestinal outcomes in CF. Methods We conducted a randomised, double-blind, placebo-controlled, two-period crossover trial (2019-2020) at Nottingham University Hospitals. The effects of TEZ/IVA on gut physiology were measured using MRI. Participants were randomly assigned to treatment sequences AB or BA (A:TEZ/IVA, B:placebo, each 28 days), with a 28-day washout period. Participants had serial MRI scans at baseline and after 19-23 days of each treatment. Due to the COVID-19 pandemic, a protocol amendment allowed for observer-blind comparisons prior to and during TEZ/IVA. In such cases, participants were not blind to the treatment but researchers remained blind. The primary outcome was oro-caecal transit time (OCTT). Secondary outcomes included MRI metrics, symptoms and stool biomarkers. Results We randomised 13 participants. Before the COVID-19 pandemic 8 participants completed the full protocol and 1 dropped out. The remaining 4 participants followed the amended protocol. There were no significant differences between placebo and TEZ/IVA for OCTT (TEZ/IVA >360minutes [225,>360] vs. placebo 330minutes [285,>360], p=0.8) or secondary outcomes. There were no adverse events. Conclusions Our data contribute to a research gap in the extra-pulmonary effects of CFTR modulators. We found no effect after TEZ/IVA on MRI metrics of gut function, GI symptoms or stool calprotectin. Effects might be detectable with larger studies, longer treatment or more effective CFTR modulators. ClinicalTrials.gov registration NCT04006873 (02/07/2019)

ABSTRACT Intracranial pathologies can affect pituitary hormone levels whether they are primarily related to the pituitary gland or not. In pituitary hormone deficiencies, the size and location of the pituitary gland may be normal, as well as one or more hormone effects may be observed after hypoplasia, partial empty sella, ectopic neurohypophysis, Rathke's cleft cyst, adenomas and other pituitary masses or surgeries. In early puberty, the pituitary dimensions are usually increased, rarely hypoplasia and rathke cleft cyst are seen as MRI findings. Non-pituitary pathologies cause especially early puberty, single or multiple pituitary hormone deficiency. In this study, we aimed to categorize the patients with pituitary/cranial lesions and hormonal pathology and to determine their frequency and distribution in the single pediatric endocrinology clinic for ten years. The data of 485 patients, 186 (38.3%) male, were retrospectively analyzed. Their mean age was 9.3 ± 3.2 years. The incidental pituitary lesion was found in 28 (5.77%) of all cases. There was no hormonal influence in 20 (71.4%) of them. In most of the 454 cases with hormonal disorders, MRI (Magnetic Resonance Imaging) findings were normal in most of the 454 cases with hormonal disorders (71.8%). The most common hormonal pathologies were precocious puberty and isolated Growth Hormone deficiency, 57.9% and 26.4% respectively. Non-pituitary lesions (n=23, 5%) were most frequently accompanied by early puberty (39.1%). The hypothalamus-pituitary axis may be affected by primary lesions in its own region, or it may be seen as a result of other cranial pathologies. Cranial evaluation is also important in addition to pituitary imaging in pituitary hormone disorders.

OBJECTIVES: This study aims to develop and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer. METHODS: This retrospective study included 263 patients with histologically proven invasive breast cancer and who underwent DCE-MRI examination before surgery in two hospitals. All patients had a defined ALN status based on pathological examination results. Regions of interest (ROIs) of the primary tumor and ipsilateral ALN were manually drawn. A total of 1,409 radiomics features were initially computed from each ROI. Next, the low variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to extract the radiomics features. The selected radiomics features were used to establish the radiomics signature of the primary tumor and ALN. A radiomics nomogram model, including the radiomics signature and the independent clinical risk factors, was then constructed. The predictive performance was evaluated by the receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA) by using the training and testing sets. RESULTS: ALNM rates of the training, internal testing, and external testing sets were 43.6%, 44.3% and 32.3%, respectively. The nomogram, including clinical risk factors (tumor diameter) and radiomics signature of the primary tumor and ALN, showed good calibration and discrimination with areas under the ROC curves of 0.884, 0.822, and 0.813 in the training, internal and external testing sets, respectively. DCA also showed that radiomics nomogram displayed better clinical predictive usefulness than the clinical or radiomics signature alone. CONCLUSIONS: The radiomics nomogram combined with clinical risk factors and DCE-MRI-based radiomics signature may be used to predict ALN metastasis in a noninvasive manner. [ABSTRACT FROM AUTHOR]

In this study, to assess the utility of whole-body DWI (WB-DWI) as an imaging biomarker for metastatic hormone-naïve prostate cancer (mHNPC), we evaluated tumor diffusion volume based on apparent diffusion coefficient (ADC) values. WB-DWI results obtained from 62 mHNPC patients were evaluated in this retrospective analysis. The association with castration resistant–free survival (CFS) was evaluated for both prostate and metastatic tumor diffusion volume (pDV and mDV, respectively) based on WB-DWI. The usefulness of pDV and mDV based on ADC values to predict CFS was also examined. During the follow-up period, 22 patients progressed to castration-resistant prostate cancer, and the median CFS was 42.6 months. The median mDV and pDV were 6.7 and 12.6 mL, respectively. mDV was a significant predictor of CFS (hazard ratio [HR]: 2.75; p = 0.022), while pDV was not significant. When DV was divided into groups by ADC values (× 10− 3 mm2/s) of 0.4–1.0 and 1.0–1.8 (× 10− 3 mm2/s), mDV with ADC values (× 10− 3 mm2/s) of 0.4–1.0 (mDV0.4−1.0) showed a more favorable association with CFS compared to total mDV. On multivariate analysis, mDV0.4−1.0 and Gleason grade group had a statistically significant association with CFS (HR: 4.0; p = 0.004, and HR: 3.4; p = 0.006, respectively), while pDV with ADC values (× 10− 3 mm2/s) of 0.4–1.0 did not have a significant association. mDV is useful for predicting CFS in mHNPC patients. mDV may be a better imaging biomarker when based on ADC values. [ABSTRACT FROM AUTHOR]

Background: Glioma is the most common primary brain tumor, and early detection of tumors is important in the treatment planning for the patient. The precise segmentation of the tumor and intratumoral areas on the MRI by a radiologist is the first step in the diagnosis, which, in addition to the consuming time, can also receive different diagnoses from different physicians. The aim of this study was to provide an automated method for segmenting the tumor and intratumoral areas. Methods: This is a fundamental-applied study that was conducted from May 2020 to September 2021 using multimodal MRI images of 285 patients with glioma tumors from the BraTS 2018 Database. This database was collected from 19 different MRI imaging centers, including multimodal MRI images of 210 HGG patients, and 75 LGG patients. In this study, a 2D U-Net architecture was designed with a patch-based method for training, which comprises an encoding path for feature extraction and a symmetrical decoding path. The training of this network was performed in three separate stages, using data from high-grade gliomas (HGG), and low-grade gliomas (LGG), and combining two groups of 210, 75, and 220 patients, respectively. Results: The proposed model estimated the Dice Similarity Coefficient (DSC) results in HGG datasets 0.85, 0.85, 0.77, LGG datasets 0.80, 0.66, 0.51, and the combination of the two groups 0.88, 0.79, 0.77 for regions the whole tumor, tumor core, and enhancing region in the training dataset, respectively. The results related to Hussdorf Distance (HD) for HGG datasets were 8.24, 9.92, 4.43, LGG datasets 11.5, 11.31, 2.23, and the combination of the two groups 7.20, 8.82, 4.43 for regions the whole tumor, tumor core, and enhancing region in the training dataset, respectively. Conclusion: Using the U-Net network can help physicians in the accurate segmentation of the tumor and its various areas, as well as increase the survival rate of these patients and improve their quality of life through accurate diagnosis and early treatment.

Objective: To determine whether L5 nerve root morphology can assist in identification Lumbosacral Transitional vertebra. Study Design: Cross sectional study. Place and Duration of Study: Radiology Department, Combined Military Hospital, Multan Pakistan, Apr 2019 to Apr 2020. Methodology: Patients of both genders, 15 to 50 years age who underwent whole spine MRI were included in the study. Patients were referred from Combined Military Hospital Multan, from neighboring Combined Military Hospitals and Civil. Sagittal and axial T1WS and T2WS were performed along with coronal T2WS/FS sequences. Axial images were assessed for identification of L5 nerve root arising from LV5-SV1 level and hence vertebra was identified as LV5. Correlation was done with sagittal images for presence of Transitional vertebra, further confirmed by counting vertebral bodies from C2 vertebra upto sacrum using cross referencing tool. Results: A total of 135 patients were included in the study. Out of these, transitional vertebra was confidently labeled in 23 patients by nerve identification method which was confirmed on vertebral counting method. However, in four patients, L5 nerve root morphology was not clear and we had to rely on vertebral counting method for identification of transitional vertebra. Conclusion: Neuroanatomy and morphology of exiting L5 nerve roots can act as a reliable method for numbering of lumbosacral vertebra and identification of transitional vertebra. [ABSTRACT FROM AUTHOR]

Introduction: Magnetic resonance imaging is the standard imaging modality for detecting disc pathology due to its advantage of lack of radiation, multiplanar imaging capability, excellent spinal soft-tissue contrast, and precise localization of intervertebral discs changes. The aim of the study is to find out the prevalence of lumbar disc degenerative disorder among patients undergoing magnetic resonance imaging in a tertiary care hospital. Methods: A descriptive cross-sectional study was conducted in the Department of Radiodiagnosis and Interventional Radiology of a tertiary care centre among patients with complaints of low back pain from 24th May, 2021 to 31st December, 2021 in a tertiary care hospital. Ethical clearance was taken from the Institutional Review Committee (Reference number: 134/2077-78). A sample size of 899 was taken and convenience sampling was done. Collected data were entered and analyzed on Statistical Package for the Social Sciences version 22.0. Point estimate at 95% Confidence Interval was calculated along with frequency and percentage for binary data and mean with standard deviation for continuous data. Results: Among 899 patients undergoing magnetic resonance imaging, the prevalence of lumbar disc degenerative disorder was found to be 155 (17.24%) (14.77-19.70 at 95% Confidence Interval). Conclusions: The prevalence of lumbar disc degenerative disorder among patients undergoing magnetic resonance in our study was lower when compared to other similar studies conducted in similar settings.

To develop a radiomic nomogram based on multiparametric magnetic resonance imaging for the preoperative prediction of lymph node metastasis (LNM) in rectal cancer. This retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram. The Radscore and clinical models exhibited similar diagnostic efficacy (DeLong's test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P : 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P : 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram. The radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy. [ABSTRACT FROM AUTHOR]

Background: The purpose of this work was to identify which Glioblastoma (GBM) problems can be handled by Magnetic Resonance Imaging (MRI) and Machine Learning (ML) techniques. Results, limitations, and trends through a review of the scientific literature in the last 5 years were performed. Google Scholar, PubMed, Elsevier databases, and forward and backward citations were used for searching articles applying ML techniques in GBM. The 50 most relevant papers fulfilling the selection criteria were deeply analyzed. The PRISMA statement was followed to structure our report., Methods: A partial taxonomy of the GBM problems tackled with ML methods was formulated with 15 subcategories grouped into four categories: extraction of characteristics from tumoral regions, differentiation, characterization, and problems based on genetics., Results: The dominant techniques in solving these problems are: Radiomics for feature extraction, Least Absolute Shrinkage and Selection Operator for feature selection, Support Vector Machines and Random Forest for classification, and Convolutional Neural Networks for characterization. A noticeable trend is that the application of Deep Learning on GBM problems is growing exponentially. The main limitations of ML methods are their interpretability and generalization., Conclusion: The diagnosis, treatment, and characterization of GBM have advanced with the aid of ML methods and MRI data, and this improvement is expected to continue. ML methods are effective in solving GBM-related problems with different precisions, Overall Survival being the hardest problem to solve with accuracies ranging from 57%-71%, and GBM differentiation the one with the highest accuracy ranging from 80%-97%., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Introduction: In developing Computer-Aided Diagnosis (CAD), a Convolutional Neural Network (CNN) has been commonly used as a Deep Learning (DL) model. Although it is still early, DL has excellent potential in implementing computers in medical diagnosis., Methods: This study reviews the use of DL for Anterior Cruciate Ligament (ACL) tear diagnosis. A comprehensive search was performed in PubMed, Embase, and Web of Science databases from 2018 to 2024. The included study criteria used MRI images to evaluate ACL tears, and the diagnosis of ACL tears was performed using the DL model. We summarized the paper by reporting their model accuracy, model comparison with arthroscopy, and explainable., Results: AI implementation in tabular format; we conclude that many medical professionals believe that arthroscopic diagnosis is the most reliable method for diagnosing ACL tears. However, due to its intrusive treatment, CAD is projected to be able to produce similar outcomes from MRI scan results. To gain the trust of physicians and meet the demand for reliable knee injury detection systems, an algorithm for CAD should also meet several criteria, such as being transparent, interpretable, explainable, and easy to use. Therefore, future works should consider creating an Explainable DL model for ACL tear diagnosis. It is also essential to evaluate the performance of this Explainable DL model compared to the gold standard of arthroscopy diagnosis., Conclusion: There are issues regarding the need for Explainable DL in CAD to increase confidence in its result while also highlighting the importance of the involvement of medical practitioners in system design. There is no funding for this work.

Objective: This study aimed to evaluate the diagnostic value of X-Map reconstruction based on Dual-Energy Computed Tomography (DECT) in acute ischemic stroke (AIS)., Methods: Sixty-six cases of suspected AIS patients hospitalized from November, 2021 to April, 2022 were retrospectively selected. DECT, Computed Tomography Perfusion imaging (CTP), Computed Tomography Angiography (CTA), and MRI were all performed within 24 hours after symptom onset. As the gold standard for diagnosing AIS, a total of 53 patients were diagnosed with AIS based on the diffusion-weighted imaging positive results in MRI. The Chi-square test was used to evaluate the diagnostic efficacy of AIS among X-Map, CTP, and CTA., Results: In the 53 patients with confirmed ASI, a total of 72 lesions were detected, including in the frontal lobes (n=33), parietal lobes (n=7), temporal lobes (n=12), basal ganglia regions (n=12), thalamus (n=3), and pons (n=5). The case detection rate of X-Map for AIS was similar to that of CTP (p=0.151) but was significantly higher than that of CTA (p<0.001). In terms of diagnostic efficacy, among the total 66 patients enrolled, X-Map achieved a higher diagnostic sensitivity (85%) than CTP and CTA. However, CTP achieved the best diagnostic specificity (84.6%) and diagnostic accuracy (77.4%) among the diagnostic tools used., Conclusion: X-Map provides a better or equal clinical value for the diagnosis of AIS as compared to CTA and CTP, respectively, highlighting its potential in clinical applications.

Background: Chronic liver disease (CLD) will affect the enhancement of hepatic parenchyma and portal vein on abdominal-enhanced MRI., Objective: To investigate the difference in liver parenchyma and portal vein enhancement in patients with CLD of different liver function grades between Gd- EOB-DTPA and Gd-DPTA in the portal venous phase (PVP)., Methods: This retrospective study included 218 patients with CLD who had undergone abdominal enhanced MRI from January 2019 to June 2020. Patients with various degrees of liver dysfunction were identified with Child-Turcotte-Pugh and albumin-bilirubin grade. Two readers measured the precontrast and PVP signal intensities of liver parenchyma, portal vein, spleen, and psoas muscle. Relative liver enhancement, liver-to-spleen contrast index, portal vein image contrast, and portal vein-to-liver contrast were calculated., Results: The relative enhancement of liver parenchyma was significantly lower for the Gd-EOB-DTPA group in any degree of liver function than the Gd- DTPA group in the PVP. The Gd-EOB-DTPA group showed significantly lower portal vein-to-liver contrast in the overall study population, CTP class B, and ALBI grade 2 patients compared to the group of Gd-DTPA at PVP. No significant difference was noted in the portal vein image contrast between the two contrast agents, regardless of CTP and ALBI grading., Conclusion: In CLD patients, Gd-EOB-DTPA yielded lower liver parenchymal enhancement and similar portal vein image contrast compared to Gd-DTPA in the PVP. Portal vein-to-liver contrast in the Gd-EOB-DTPA group was lower in the CTP class B and ALBI grade 2 subgroups compared to the Gd- DTPA group., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Although the essential components of pain pathways have been identified, a thorough comprehension of the interactions necessary for creating focused treatments is still lacking. Such include more standardised methods for measuring pain in clinical and preclinical studies and more representative study populations., Objective: This review describes the essential neuroanatomy and neurophysiology of pain nociception and its relation with currently available neuroimaging methods focused on health professionals responsible for treating pain., Methods: Conduct a PubMed search of pain pathways using pain-related search terms, selecting the most relevant and updated information., Results: Current reviews of pain highlight the importance of their study in different areas from the cellular level, pain types, neuronal plasticity, ascending, descending, and integration pathways to their clinical evaluation and neuroimaging. Advanced neuroimaging techniques such as fMRI, PET, and MEG are used to better understand the neural mechanisms underlying pain processing and identify potential targets for pain therapy., Conclusions: The study of pain pathways and neuroimaging methods allows physicians to evaluate and facilitate decision-making related to the pathologies that cause chronic pain. Some identifiable issues include a better understanding of the relationship between pain and mental health, developing more effective interventions for chronic pain's psychological and emotional aspects, and better integrating data from different neuroimaging modalities for the clinical efficacy of new pain therapies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)