Current status of pediatric radiology in India: a perspective.

This review is a bird's eye view of the practice of pediatric radiology in India. The key focus of this article is training, certification and employment opportunities for radiologists aspiring to specialise in pediatric radiology. Further, we have traced the growth in academic and research opportunities over the past two decades, as well as given a peep into the future trajectory of this speciality. An understanding of these concepts is key to the expansion of pediatric radiologists not just within India, but across the globe.

Endemic pediatric fungal infections in India: clues to diagnosis.

This review is intended to familiarize readers with an emerging group of fungal infections that mostly manifest in immunocompetent individuals. This group was initially considered endemic to the tropics, but increasing worldwide prevalence has been reported. The organisms have been divided into dominant non-invasive forms and dominant invasive forms for ease of understanding. The non-invasive organisms include the group Entomophthoromycota, under which two genera Basidiobolus and Conidiobolus, have been identified as human pathogens. They present with plaques in the extremities and rhinofacial region, respectively. The invasive organisms are dematiaceous fungi (phaeohypomycosis), which includes Cladophialophora and Exophiala among others. They cause invasion of deep tissues, with the central nervous system being the most common target. The mycology, epidemiology, diagnosis, and treatment options have been summarized in brief. The clinical presentation, imaging manifestations, differentiation from other common infections and malignancies that show similar features have been detailed.

Diagnostic accuracy of whole-body magnetic resonance imaging versus positron emission tomography-computed tomography for the staging of pediatric lymphoma: a systematic review and meta-analysis.

BACKGROUND: Whole-body magnetic resonance imaging (MRI) has been investigated by multiple authors as a radiation-free alternative to positron emission tomography computed tomography (PET-CT) in children with lymphoma. OBJECTIVE: To evaluate the sensitivity, specificity, and diagnostic odds ratio of whole-body MRI compared to PET-CT for the staging of pediatric lymphoma. METHODS: The databases PubMed, Embase, and Scopus were searched for studies that reported the accuracy of whole-body MRI compared to PET-CT for lymphoma staging in children. Data was collected from included studies to formulate 2 × 2 contingency tables, including the number of true positive, true negative, false positive, and false negative. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated. Summary receiver operating characteristic curves were drawn and the area under the curve (AUC) calculated. In addition, the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS 2) tool was used to assess the risk of bias and applicability concerns. RESULTS: A total of seven studies were included in the final analysis. Of these, six studies used unenhanced whole-body MRI. The pooled sensitivity of whole-body MRI-based staging was 95.8%, while the pooled specificity was 21.8%. The DOR for whole-body MRI was 1.19. For extranodal staging, the pooled sensitivity was 88.9%, specificity was 97.4%, and DOR was 25.29. The partial AUC for overall staging was 0.63, whereas that for extranodal staging stood at 0.88. Based on the QUADAS 2 tool, all seven studies were at risk of bias (six at high risk, one at unclear risk). CONCLUSION: Whole-body MRI has high sensitivity for staging of pediatric lymphoma and may be a useful alternative to PET-CT.

Can Whole-Body Magnetic Resonance Imaging Predict Relapse in Juvenile Idiopathic Arthritis? A Longitudinal Pilot Study.

OBJECTIVE: To determine the utility of whole-body magnetic resonance imaging (WB MRI) to predict relapse in children with juvenile idiopathic arthritis (JIA) in clinical remission. METHODS: Consecutive patients with JIA who fulfilled the Wallace criteria for remission were recruited into this longitudinal pilot study and underwent WB MRI. A radiological score was devised, incorporating synovitis, bone marrow edema, sacroiliitis, enthesitis, and bone erosions. Two readers independently scored the MR data sets. The same score was calculated for both knee joints individually and correlated with outcome for that joint. Score-based models incorporating clinical and laboratory variables were generated. Logistic regression analysis was done to determine predictors for relapse. Receiver operating characteristic curve was drawn for significant variables. RESULTS: Twenty-two children (median age, 12 years; interquartile range, 9.5-14.25 years) were included in the final analysis. At 24 months' follow-up, 15 joints in 5 children relapsed; knee was the most common site. Seven knee joints had disease relapse. On univariate analysis, synovitis and total score on WB MRI were significant predictors of relapse at follow-up, with odds ratios of 9.46 (bias-corrected 95% confidence interval, 3.07-29.13) and 2.8 (bias-corrected 95% confidence interval, 1.23-6.39) respectively. Two models, which included a higher number of joints involved at presentation and abrupt drug withdrawal strategy as predictor variables, were also statistically significant (odds ratio, approximately 1.9). On multivariate analysis of the predictors variables in models where p < 0.6, it was found that only synovitis score and total score were near statistical significance ( p = 0.06); no clinical or laboratory variables were significant. The areas under the receiver operating characteristic curve for relapse prediction were approximately 0.82, 0.87, 0.79, and 0.81 for synovitis score, total MRI score, and both models, respectively. CONCLUSION: Synovitis on WB MRI is the strongest independent predictor for disease relapse in children with JIA in remission.

Transcutaneous Mediastinal Ultrasonography for Lymphadenopathy in Children: A Pictorial Essay of Technique and Imaging Findings.

Ultrasonography (US) forms the mainstay of imaging in children; however, in the chest, its use has traditionally been limited to evaluation of pleural pathology. US techniques such as endobronchial and endoscopic ultrasound, which are commonly used for detection of mediastinal lymphadenopathy are invasive, aerosol generating, and often require sedation. Transcutaneous mediastinal sonography (TMUS) offers a useful alternative, which is easier to perform and overcomes these limitations. In this review, we summarize the technique, as well as imaging appearances of lymph nodes on TMUS. We also list common problems faced by operators and suggest troubleshooting methods for these.

Pediatric lung ultrasonography: current perspectives.

Ultrasonography (US) is the workhorse of pediatric imaging; however, lung US is only a recently developed application. US of the lung is based predominantly on the imaging of chest wall-air-fluid interfaces. In this review, we summarize the available literature on applications of lung US in neonatal as well as pediatric care. We describe the imaging appearance of various commonly encountered pathologies including pneumonia and respiratory distress syndrome, among others, and provide illustrative images. Finally, we describe the limitations of the technique that are essential knowledge for radiologists, critical care physicians, sonographers and technologists attempting to use lung US effectively for diagnosis and management.

Imaging in extremity vascular trauma: can MDCT angiography predict the nature of injury?

OBJECTIVE: This study aims to evaluate the utility of computed tomography angiography (CTA) signs of vascular injury in the differentiation of vessel transection from pure thrombosis with intact vessel wall. METHODS: Retrospective analysis was done on 146 consecutive patients who had undergone CTA and surgical exploration from January 2015 to September 2019. Twelve imaging parameters were assessed. Chi-square was used to test the difference between groups. In addition, a scoring system was devised where one point each was added for the presence of 5 signs and absence of 3 signs. ROC analysis was done for the variables which had shown significant difference between groups and for the composite score. RESULTS: On surgical exploration, 87 patients had transection of vessel, while 59 had thrombosis. Significant difference was found among the two groups in non-opacification, pseudoaneurysm, extravasation (p = 0.04 each), thrombosed cord (p < 0.001), collaterals (p = 0.001) and hematoma (p = 0.002), while other signs did not show significant difference. The AUC value for each of these variables was < 0.650, while for the score, AUC was .843(.773-.913). A cut-off value of ≥ 1.5 gave 83.1% sensitivity and 70% specificity for diagnosing transection. CONCLUSION: CTA is a useful tool to classify the nature of vascular injury. It is advisable to use a composite score for maximum diagnostic value.

Can imaging suggest the aetiology in skull base osteomyelitis? A systematic literature review.

PURPOSE: To assess differentiating features between bacterial, Aspergillus, and Mucor skull base osteomyelitis (SBO) with regard to clinical presentation and imaging appearances. MATERIAL AND METHODS: A literature search was performed in April 2020 for studies on SBO with a minimum sample size of 10 patients. Studies that reported presenting symptoms, cross-sectional imaging findings, complications, and mortality were included in the analysis. The quality of included articles was tested using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. A data extraction form was used to retrieve relevant parameters from each of the articles. RESULTS: Thirteen articles were included in the final analysis. Diabetes mellitus was the most common predisposing factor (12.5-91.0%). Presenting complaints in all bacterial SBO studies were otogenic, while fungal SBO patients had nasal/ocular complaints. Rates of mortality and surgical intervention in the fungal group were 50-100% and 50%, respectively, as compared to the bacterial group - 7-87% and 10%, respectively. On imaging, the site of initial infection in bacterial SBO was the external auditory canal, while in fungal SBO it was the paranasal sinus. The incidence of orbital extension was < 5% in bacterial and 44-70% in fungal SBO, among which Mucor had rates of 65-70%. Bone erosion was less extensive in bacterial SBO, and the patterns differed. The highest incidence of vascular involvement and non-enhancing lesions (23-36%) was seen in Mucor. Aspergillus showed highest sino-cranial extension (52-55%) and homogenous bright enhancement. CONCLUSIONS: Systematic analysis of the clinico-radiological parameters in each of the studies revealed differences in presentation, clinical course, extension, bone erosion, and enhancement.

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.

Background Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. Purpose To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. Methods The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. Results A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919-0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. Conclusion While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.