Indings that could be attributable to COVID-19 pneumonia, requiring radiologists to decide whether or not to mention COVID-19 specifically as a differential diagnostic possibility. We aim to provide guidance to radiologists in reporting CT findings potentially attributable to COVID-19 pneumonia, including standardized language to reduce reporting variability when addressing the possibility of COVID-19. When typical or indeterminate features of COVID-19 pneumonia are present in endemic areas as an incidental finding, we recommend contacting the referring providers to discuss the likelihood of viral infection. These incidental findings do not necessarily need to be reported as COVID-19 pneumonia. In this setting, using the term “viral pneumonia” can be a reasonable and inclusive alternative. However, if one opts to use the term “COVID-19” in the incidental setting, consider the provided standardized reporting language. In addition, practice patterns may vary, and this document is meant to serve as a guide. Consultation with clinical colleagues at each institution is suggested to establish a consensus reporting approach. The goal of this expert consensus is to help radiologists recognize findings of COVID-19 pneumonia and aid their communication with other healthcare providers, assisting management of patients during this pandemic. S.S. and F.U.K. are co-first authors. Published under a CC BY 4.0 license. The authors declare no conflicts of interest. Correspondence to: Harold Litt, MD, PhD, Department of Radiology, Perelman School of Medicine of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104 (e-mail: harold.litt@uphs.upenn.edu). Article was originally published in Radiology: Cardiothoracic Imaging. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved...

OBJECTIVE. A substantial proportion of cases of usual interstitial pneumonia (UIP) are due to connective tissue disease (CTD)-associated interstitial lung disease (ILD). The purpose of this study was to determine whether specific CT findings can help differentiate a UIP pattern of CTD-ILD from a UIP pattern of idiopathic pulmonary fibrosis (IPF) and whether these signs are associated with survival. MATERIALS AND METHODS. Adults visiting an ILD clinic from 2006 to 2015 enrolled in a research registry with a multidisciplinary diagnosis of CTD-ILD or IPF and a UIP pattern at high-resolution CT were included in the study. In these subjects with CT findings of UIP due to either IPF or CTD-ILD, three CT findings anecdotally associated with CTD-ILD were assessed for diagnostic accuracy: the “straight-edge” sign, the “exuberant honeycombing” sign, and the “anterior upper lobe” sign. Survival assessments were performed with univariate and multivariable techniques. RESULTS. The subjects included 63 patients who had...

Compared with vendor-supplied respiratory-gating hardware methods, software gating performed favorably, both qualitatively and quantitatively. PurposeTo assess the performance of hardware- and software-gating technologies in terms of qualitative and quantitative characteristics of respiratory motion in positron emission tomography (PET) imaging.Materials and MethodsBetween 2010 and 2013, 219 fluorine 18 fluorodeoxyglucose PET examinations were performed in 116 patients for assessment of pulmonary nodules. All patients provided informed consent in this institutional review board–approved study. Acquisitions were reconstructed as respiratory-gated images by using hardware-derived respiratory triggers and software-derived signal (via an automated postprocessing method). Asymmetry was evaluated in the joint distribution of reader preference, and linear mixed models were used to evaluate differences in outcomes according to gating type.ResultsIn blind reviews of reconstructed gated images, software was selected as superior 16.9% of the time (111 of 657 image sets; 95% confidence interval [CI]: 14.0%, 19.8%), and hardware was selected as superior 6.2%...

OBJECTIVE. The objective of this study is to determine the CT findings and patterns of interstitial pneumonia with autoimmune features (IPAF) and to assess whether imaging can predict survival for ...

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96–4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66–4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (−88 ml/yr vs. −45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.