背景：间质性肺异常（ILA）与更严重的临床结果有关，但通过肺癌筛查CT检查检测到的ILA尚未得到定量评估。目的：通过使用基于深度学习的纹理分析，确定韩国国家肺癌筛查计划CT检查中ILA的患病率，并定义最佳肺部面积阈值以便进行ILA检测。材料和方法：本回顾性研究纳入参加韩国国家肺癌筛查计划的两个医疗中心于2017年4月至2020年12月之间进行胸部CT检查的参与者。三名放射科医生对CT表现进行分类，分为三组：无ILA、不确定型ILA和ILA（纤维性和非纤维性）。通过基于深度学习的纹理分析进行定性和定量评估ILA的范围。使用Youden指数确定纹理分析检测ILA的最佳截断值。比较进展性ILA和非进展性ILA的人群的人口统计学和ILA亚类别。结果：共纳入3118名参与者进行分析，其中有120名（4％）参与者CT检查显示出ILA。定量系统计算的ILA中位数范围为：ILA组为5.8％、不确定型ILA组为0.7％、无ILA组为0.1％（P＜0.001）。肺部区域1.8％的面积阈值能够完全检测出ILA，灵敏度为100％，特异度为99％。视觉评估的纤维性ILA中观察到48％的进展性（31例中的15例），但在进展者中，ILA的定量范围增加了3.1％。结论：在韩国肺癌筛查人群中，检测到4％的ILA。基于深度学习的纹理分析在使用1.8％肺部面积截断值进行ILA检测时具有高灵敏度和特异度。 © RSNA, 2023本文提供了补充资料。此外，本文编辑观点由江头和西野在本期中讨论。

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.