Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.

Young patients presenting with thrombotic events like pulmonary embolism and cardiological phenomenon such as presence of an intracardiac mass, without any underlying risk factors, should be promptly investigated for thrombophilias including antiphospholipid syndrome (APLS). This case is reported to highlight rare occurrence of co-existing bilateral extensive pulmonary embolism and an intra-cardiac mass at presentation of antiphospholipid syndrome as well as progression to near catastrophic APLS.

Background: Breast cancer is the most common type of tumors in Pakistani women, with axillary lymph node (ALN) positivity reported to be one of the most important prognostic factors.This study shows the distribution of various clinical and pathological variables including age, tumor size, grade, histologic subtype, and hormone receptor status among Pakistani women with and without ALN metastasis.Materials and Methods: A total of 245 cases of primary breast cancer from Northern Pakistan were analyzed in this study. Their clinical, pathological and immunohistochemical parameters, including estrogen receptor (ER), progesterone receptor (PR) and Her-2/Neu status, were extracted from previous histopathological reports and stratified based on the occurrence of ALN metastasis. Results: Occurrence of ALN metastasis was significantly different between older age patients above 50 years and younger age patients age <50 years [χ² (1, N=245) =14.6, p<0.001]. There was an increased number of metastases in large sized tumors >5cm in size (80%, n=60), [χ² (2, N=245) =23.1, p<0.001] and tumors with higher nuclear grade III (78.4%, n=40), [χ² (1, N=245) =5.1, p=0.02]. ALN metastasis was inversely associated with expression of estrogen receptor [χ² (1, N=245) =12.5, p<0.001], and progesterone receptor [χ² (1, N=245) = <0.001, p=0.99], while it was directly associated with Her-2/Neu expression [χ² (1, N=245) =5.63, p=0.01]. Conclusion: In Pakistani women, ALN metastasis was significantly associated with older age, tumor size, and high-grade tumors showing Her2/Neu expression and was inversely associated with ER, PR expression in breast tumors.

Objective: To evaluate the temporal changes on serial chest radiographs (CXRs)of hospitalised COVID-19 positive patients till their outcome(discharge/death); to determine the severity of CXR score and its correlation with clinical outcome (hospital stay, chest intubation and mortality). Study Design: Descriptive study. Place and Duration of Study: Shifa International Hospital (SIH), Islamabad from March to June 2020. Methodology: After IRB approval, 112 patients were consecutively enrolled, having laboratory-confirmed SARS-CoV-2 and hospitalised in SIH. Patients' demographics and clinical data were retrieved from Radiology Information System (RIS). Chest radiographs (CXR) were retrieved from picture archive and communication system (PACS). CXR severity scoring was determined by three radiologists, and results were analysed. Results: Lung opacities (98.2%), involvement of both lungs (96.4%), both peripheral and central region involvement (62.5%) and upper/mid/lower zone distribution (61.6%) were the most frequent findings. Males affected more than females with a mean age of 58.9 ± 13.1 years. Zonal involvement, density and extent of opacities peaked on 10-13 th day of illness. In the last CXR, opacities showed decrease in extent as well as density, reduction in zonal involvement, and few having mixed interstitial thickening/fibrosis. One hundred and five out of 112 (93.8%) patients had residual radiographic abnormalities on discharge. Conclusion: Serial chest radiography can be used to monitor disease progression and temporal changes after initial HRCT. Patients who have CXR severity score of 4 or more at the time of admission, is a red flag for prolonged hospital stay and possible intubation. Severity of CXR findings peaked at 10-13 days. It is recommended to repeat CXRs every 3-4 th day during hospital stay. Majority of the patients has residual radiographic abnormality on discharge.

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