Although a definite diagnosis of viral pneumonia cannot be made on the basis of typical imaging features alone, a combination of clinical and radiologic findings can be suggestive of potential pathogens. Viruses are the most common causes of respiratory infection. The imaging findings of viral pneumonia are diverse and overlap with those of other nonviral infectious and inflammatory conditions. However, identification of the underlying viral pathogens may not always be easy. There are a number of indicators for identifying viral pathogens on the basis of imaging patterns, which are associated with the pathogenesis of viral infections. Viruses in the same viral family share a similar pathogenesis of pneumonia, and the imaging patterns have distinguishable characteristics. Although not all cases manifest with typical patterns, most typical imaging patterns of viral pneumonia can be classified according to viral families. Although a definite diagnosis cannot be achieved on the basis of imaging features alone, recognition of viral pneumonia patterns may aid in differentiating viral pathogens, thus reducing the use of antibiotics. Recently, new viruses associated with recent outbreaks including human metapneum...

Background Interstitial lung abnormalities (ILAs) are associated with worse clinical outcomes, but ILA with lung cancer screening CT has not been quantitatively assessed. Purpose To determine the prevalence of ILA at CT examinations from the Korean National Lung Cancer Screening Program and define an optimal lung area threshold for ILA detection with CT with use of deep learning-based texture analysis. Materials and Methods This retrospective study included participants who underwent chest CT between April 2017 and December 2020 at two medical centers participating in the Korean National Lung Cancer Screening Program. CT findings were classified by three radiologists into three groups: no ILA, equivocal ILA, and ILA (fibrotic and nonfibrotic). Progression was evaluated between baseline and last follow-up CT scan. The extent of ILA was assessed visually and quantitatively with use of deep learning-based texture analysis. The Youden index was used to determine an optimal cutoff value for detecting ILA with use of texture analysis. Demographics and ILA subcategories were compared between participants with progressive and nonprogressive ILA. Results A total of 3118 participants were included in this study, and ILAs were observed with the CT scans of 120 individuals (4%). The median extent of ILA calculated by the quantitative system was 5.8% for the ILA group, 0.7% for the equivocal ILA group, and 0.1% for the no ILA group (P < .001). A 1.8% area threshold in a lung zone for quantitative detection of ILA showed 100% sensitivity and 99% specificity. Progression was observed in 48% of visually assessed fibrotic ILAs (15 of 31), and quantitative extent of ILA increased by 3.1% in subjects with progression. Conclusion ILAs were detected in 4% of the Korean lung cancer screening population. Deep learning-based texture analysis showed high sensitivity and specificity for detecting ILA with use of a 1.8% lung area cutoff value. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Egashira and Nishino in this issue.

The purpose of this study was to describe the radiologic findings of computed tomography (CT), magnetic resonance (MR) imaging, and ¹⁸F-fluorodeoxy glucose positron emission tomography (FDG PET) in desmoid-type fibromatosis of the thorax. We retrospectively evaluated 47 consecutive patients with pathologically proven desmoid-type fibromatosis from January 2005 to March 2015. Patients underwent CT (n = 36) and/or MR (n = 32), and 13 patients also underwent FDG PET. Based on CT and MR, the sizes, locations, margins, contours, presence of surrounding fat, extra-compartment extension, bone involvement, and neurovascular involvement of the tumors were recorded. The attenuation, signal intensity, enhancement pattern, and presence of internal low signal band or signal void of the tumors were evaluated. Initial image findings were then compared between 2 groups of tumors: group 1 with recurrence or progression, and group 2 with no recurrence or stable without treatment. Median age at diagnosis of the tumors was 45 years, range 4 to 96, female-to-male ratio 1.8. Median tumor long diameter was 65 mm (range, 22-126 mm). The most common locations were chest wall (42.6%), followed by supraclavicular area, shoulder or axillary area, and mediastinum. The tumors had well-defined margins (83.0%), lobulated in contours (66.0%) surrounding fat (63.8%), extra-compartment extensions (42.6%), bone involvements (42.6%), and neurovascular involvements (27.7%). On CT, tumors had low attenuation (60.0%) with mild enhancement (median 24 HU, range 0-52). On MR, they showed iso-signal intensity (SI) (96.9%) on T1-weighted images (WI), and high SI (90.6%) on T2WI images, with strong (87.5%) and heterogeneous (96.9%) enhancement. Internal low signal bands (84.4%) and signal voids (68.8%) were noted. The median value of maxSUV was 3.1 (range, 2.0-7.3). In group 1 (n = 19, 40.4%), 13 patients suffered recurrence and 6 experienced progression. Group 2 (n = 28, 59.6%) consisted of 21 patients with no recurrence and 7 stable patients receiving no treatment. Partially ill-defined margins (OR, 0.167; 95% CI 0.029-0.943; P = 0.043) was the independent predictor for recurrence or progression of tumor. Knowledge of the radiological findings in desmoid-type fibromatosis on CT, MR, and FDG PET may help to improve diagnosis. Tumors with partially ill-defined margins have a tendency to recur or progress.