The corpus callosum is a midline cerebral structure and has a unique embryological development pattern. In this article, we describe the pathophysiology and present imaging findings of various typical/atypical conditions affecting the corpus callosum. Since many of these pathologies have characteristic appearances on magnetic resonance imaging (MRI) and their therapeutic approaches are poles apart, ranging from medical to surgical, the neuroradiologist should be well aware of them.

Cervical cancer is a common gynecological malignancy and a frequent cause of death. Patient outcome depends on tumor stage, size, nodal status, and histological grade. Correct tumor staging is important to decide the the treatment strategy. Magnetic Resonance Imaging is accepted as a preferred imaging modality to assess the prognostic factors.

Although hilar cholangiocarcinoma is relatively rare, it can be diagnosed on imaging by identifying its typical pattern. In most cases, the tumor appears to be centered on the right or left hepatic duct with involvement of the ipsilateral portal vein, atrophy of hepatic lobe on that side, and invasion of adjacent liver parenchyma. Multi-detector computed tomography (MDCT) and magnetic resonance cholangiopancreatography (MRCP) are commonly used imaging modalities to assess the longitudinal and horizontal spread of tumor.

Context Computerized tomography (CT) is an invaluable imaging investigation for evaluating COVID-19 disease. CT detects early changes of COVID-19 pneumonia and predicts the disease prognosis based on a semiquantitative 25-point CT severity score (CT-SS). India launched its vaccination drive in January 2021 with two different vaccines being approved by the government. These vaccines are believed to prevent the disease itself, in majority of the cases and at least decrease disease severity, in the rest. Aim This study aims to evaluate the CT-SS in vaccinated and non-vaccinated subjects who have been diagnosed with COVID-pneumonia or are COVID suspects. Subjects and Methods A total of 3,235 patients with typical COVID-19 related imaging findings on HRCT thorax were included in the study. These subjects were divided into three age categories, 18–44, 45–59 and ≥60 years. The CT severity scores were allotted by experienced radiologists. Medians of the scores in different age groups were compared amongst vaccinated and non-vaccinated individuals using the Kruskal–Wallis H test. A p-value < 0.05 was considered significant. All results were shown with 95% confidence interval. Results The difference in the medians amongst the vaccinated and non-vaccinated groups was significant, p-values being < 0.001 in all age categories. Conclusion The mean CT-SS was less in vaccinated subjects and the difference in median CT-SS amongst vaccinated and non-vaccinated individuals was statistically significant, thus sending an important message that it is mandatory for the population at large to get vaccinated to reduce infection rate/disease severity.

Context: As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists. Aim: This study evaluates and compares the performance of an artificial intelligence (AI) system with a radiologist in detecting chest radiograph findings due to COVID-19. Subjects and Methods: The test set consisted of 457 CXR images of patients with suspected COVID-19 pneumonia over a period of three months. The radiographs were evaluated by a radiologist with experience of more than 13 years and by the AI system (NeuraCovid, a web application that pairs with the AI model COVID-NET). Performance of AI system and the radiologist were compared by calculating the sensitivity, specificity and generating a receiver operating characteristic curve. RT-PCR test results were used as the gold standard. Results: The radiologist obtained a sensitivity and specificity of 44.1% and 92.5%, respectively, whereas the AI had a sensitivity and specificity of 41.6% and 60%, respectively. The area under curve for correctly classifying CXR images as COVID-19 pneumonia was 0.48 for the AI system and 0.68 for the radiologist. The radiologist's prediction was found to be superior to that of the AI with a P VALUE of 0.005 . Conclusion: The specificity and sensitivity of detecting lung involvement in COVID-19, by the radiologist, was found to be superior to that by the AI system.