Lee Jong Hyuk, Chae Kum Ju, Lu Michael T, Chang Yeun-Chung, Lee Seungho, Goo Jin Mo, Choi Seung Ho, Kim Hyungjin
Department of Radiology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Korea.
Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Radiology. 2025 Aug;316(2):e243393. doi: 10.1148/radiol.243393.
Background Sybil, an open-source deep learning model that uses low-dose CT (LDCT) for lung cancer prediction, requires rigorous external testing to confirm generalizability. Additionally, its utility in identifying individuals with high risk who never smoked or have light smoking histories remains unanswered. Purpose To externally test Sybil for identifying individuals with high risk for lung cancer within an Asian health checkup cohort. Materials and Methods This retrospective study analyzed LDCT scans from a single medical checkup facility in a study sample of individuals aged 50-80 years, collected between January 2004 and December 2021, with at least one follow-up scan. The predictive performance of the model for lung cancer risk over a 6-year period was assessed using the time-dependent area under the receiver operating characteristic curve (AUC). These evaluations were conducted in the overall study sample and within subgroups of patients with heavy (at least 20 pack-years) and never- or light smoking histories (ie, ever smoking [median, 2 pack-years]; ineligible for lung cancer screening per 2021 U.S. Preventive Services Task Force recommendations). Additionally, performance was evaluated according to the visibility of lung cancers on baseline LDCT scans. Results: Among 18 057 individuals (median age, 56 years [IQR, 52-61 years]; 11 267 male), 92 lung cancers were diagnosed (0.5%) within 6 years. Of these, 2848 had heavy smoking histories and 9943 had never- or light smoking histories, with 24 (0.8%) and 41 (0.4%) lung cancers, respectively. Sybil achieved AUCs of 0.91 for 1-year risk and 0.74 for 6-year risk. In the heavy-smoking subgroup, 1-year AUC was 0.94 (for visible lung cancers) and 6-year AUC was 0.70 (for future lung cancers). For the never- or light-smoking subgroup, Sybil had an AUC of 0.89 for visible lung cancers and 0.56 for future lung cancers. Conclusion: Sybil demonstrated excellent discriminative performance for visible lung cancers and acceptable performance for future lung cancers in Asian individuals with heavy smoking history but demonstrated poor performance for future lung cancers in a never- or light-smoking subgroup. © RSNA, 2025 See also the editorial by Jacobson and Byrne in this issue.
Sybil是一种使用低剂量CT（LDCT）进行肺癌预测的开源深度学习模型，需要进行严格的外部测试以确认其通用性。此外，其在识别从不吸烟或有轻度吸烟史的高危个体中的效用仍未得到解答。目的：在亚洲健康体检队列中对Sybil进行外部测试，以识别肺癌高危个体。材料与方法：这项回顾性研究分析了2004年1月至2021年12月期间从一家医疗检查机构收集的年龄在50 - 80岁、至少有一次随访扫描的个体的LDCT扫描数据。使用时间依赖性受试者操作特征曲线（AUC）下的面积评估该模型在6年期间对肺癌风险的预测性能。这些评估在整个研究样本以及有重度（至少20包年）吸烟史和从不吸烟或轻度吸烟史（即曾经吸烟[中位数，2包年]；根据2021年美国预防服务工作组建议不符合肺癌筛查条件）的患者亚组中进行。此外，根据基线LDCT扫描上肺癌的可见性评估性能。结果：在18057名个体（中位年龄，56岁[四分位间距，52 - 61岁]；11267名男性）中，6年内诊断出92例肺癌（0.5%）。其中，2848人有重度吸烟史，9943人有从不吸烟或轻度吸烟史，分别有24例（0.8%）和41例（0.4%）肺癌。Sybil在1年风险时的AUC为0.91，6年风险时为0.74。在重度吸烟亚组中，1年AUC为0.94（对于可见肺癌），6年AUC为0.70（对于未来肺癌）。对于从不吸烟或轻度吸烟亚组，Sybil对于可见肺癌的AUC为0.89，对于未来肺癌为0.56。结论：Sybil在有重度吸烟史的亚洲个体中对可见肺癌表现出优异的鉴别性能，对未来肺癌表现出可接受的性能，但在从不吸烟或轻度吸烟亚组中对未来肺癌表现不佳。©RSNA，2025 另见本期Jacobson和Byrne的社论。
