Coronavirus Disease-2019 (COVID-19) originated in the Wuhan, Hubei Province, China in November 2019 and has since been declared a pandemic by the WHO. COVID-19 is an acute infectious disease, primarily affecting the respiratory system. Currently, real-time reverse transcription polymerase chain reaction (RT-PCR) performed on respiratory specimens is considered the reference by which to diagnose COVID-19. However, the limitations of RT-PCR, specifically, the fact that it is time-consuming and inadequate for the assessment of disease severity, have affected the process of epidemiological disease containment and has taken a toll on the healthcare management chain. As the risk of infection for other patients and personnel must be kept to a minimum, the indications for imaging have to be carefully considered. Imaging is primarily performed in patients with a negative RT-PCR, but a high clinical suspicion of COVID-19, or, in patients with diagnosed COVID-19 who are suffering from moderate to severe symptoms. In this article, we review the typical imaging findings in COVID-19, the differential diagnoses, and common complications.

Objectives: To explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. Materials and Methods: Retrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis—by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis—was performed to predict clinical outcomes. Results: The study enrolled 52 patients who presented with variable FIGO stages in the age range of 28–79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7–76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.

Background: SMARCA4/BRG1 protein–deficient lung adenocarcinomas and thoracic sarcoma are recently described entities that lack distinctive histological features, transcription termination factor 1 (TTF1) reactivity, and actionable driver mutations. The current diagnostic path for small lung biopsies as recommended by the World Health Organization (WHO, 2015) is likely to categorize these as non– small cell carcinoma–not otherwise specified (NSCC-NOS). The present study attempts to define the subtle but distinctive clinicopathologic features of SMARCA4/BRG1 protein-deficient thoracic tumors; highlight their unique biology; and addresses the unmet need to segregate these using a new, tissue-proficient diagnostic pathway.Methods: All lung biopsies and those from metastatic sites in patients with suspected advanced lung cancer and classified as NSCC-NOS as per WHO (2015) guidelines were subjected to BRG1 testing by immunohistochemistry. SMARCA4/BRG1 protein–deficient thoracic tumors were evaluated by an extended immunohistochemistry panel. Predictive biomarker and programmed death–ligand 1 testing was conducted in all cases.Results: Of 110 cases, nine were found to be SMARCA4/BRG1 protein-deficient; six were identified as SMARCA4/BRG1 protein–deficient lung adenocarcinomas, and three were SMARCA4/BRG1 protein-deficient thoracic sarcomas. The histology ranged from poorly differentiated to undifferentiated to rhabdoid. None of the cases showed significant expression of TTF1 or p40, and no actionable mutation was identified.Conclusions: It is difficult to separate BRG1-deficient lung adenocarcinomas and thoracic sarcomas based on morphology alone. We propose a diagnostic pathway for small biopsies of thoracic tumors to segregate these distinct entities so that they can be studied more efficaciously for new biomarkers and therapeutic options.

We aimed to use quantitative values derived from perfusion and diffusion-weighted MR imaging (PWI and DWI) to differentiate radiation-induced necrosis (RIN) from tumor recurrence in Glioblastoma (GBM) and investigate the best parameters for improved diagnostic accuracy and clinical decision-making. Methods: A retrospective analysis of follow-up MRI with new enhancing observations was performed in histopathologically confirmed subjects of post-treated GBM, who underwent re-surgical exploration. Quantitative estimation of rCBV (relative cerebral blood volume) from PWI and three methods of apparent diffusion coefficient (ADC) estimation were performed, namely ADC R1 (whole cross-sectional area of tumor), ADC R2 (only solid enhancing lesion), and ADC R3 (central necrosis). ROC curve and logistic regression analysis was completed. A confusion matrix table created using Excel provided the best combination parameters to ameliorate false-positive and false-negative results. Results: Forty-four subjects with a mean age of 46 years (range, 19–70 years) underwent re-surgical exploration with RIN in 28 (67%) and recurrent tumor in 16 (33%) on histopathology. rCBV threshold of >3.4 had the best diagnostic accuracy (AUC = 0.93, 81% sensitivity and 89% specificity). A multiple logistic regression model showed significant contributions from rCBV (p < 0.001) and ADC R3 (p = 0.001). After analysis of confusion matrix ADC R3 > 2032 × 10⁻⁶ mm² achieved 100% specificity with gain in sensitivity (94% vs. 56%). Conclusions: A combination of parameters had better diagnostic performance, and a stepwise combination of rCBV and ADC R3 obviated unnecessary biopsies in 10% (3/28), leading to improved clinical decision-making.