Case 291: Amyloid-associated Cystic Lung Disease and Coexistent Mucosa-associated Lymphoid Tissue Lymphoma.

History A 46-year-old woman with known mixed connective tissue disease with clinical features of scleroderma and polymyositis and who was not on specific medications was referred to our institution to assess for interstitial lung disease due to her predisposing condition. She was a nonsmoker, had no respiratory symptoms, and enjoyed good exercise tolerance. She did not have any cutaneous lesions or renal disease.

Case 291.

History A 46-year-old woman with known mixed connective tissue disease with clinical features of scleroderma and polymyositis and who was not on specific medications was referred to our institution to assess for interstitial lung disease due to her predisposing condition. She was a nonsmoker, had no respiratory symptoms, and enjoyed good exercise tolerance. She did not have any cutaneous lesions or renal disease.

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

Purpose: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR).

Materials And Methods: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives).

Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis.

Purpose: The coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic. CT although sensitive in detecting changes suffers from poor specificity in discrimination from other causes of ground glass opacities (GGOs). We aimed to develop and validate a CT-based radiomics model to differentiate COVID-19 from other causes of pulmonary GGOs.

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting.

Objectives: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.

Methods: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435).

Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19.

Background Current coronavirus disease 2019 (COVID-19) radiologic literature is dominated by CT, and a detailed description of chest radiography appearances in relation to the disease time course is lacking. Purpose To describe the time course and severity of findings of COVID-19 at chest radiography and correlate these with real-time reverse transcription polymerase chain reaction (RT-PCR) testing for severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, nucleic acid. Materials and Methods This is a retrospective study of patients with COVID-19 confirmed by using RT-PCR and chest radiographic examinations who were admitted across four hospitals and evaluated between January and March 2020.