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. 2025 Aug 5;316(2):e243393. doi: 10.1148/radiol.243393

Figure 1:

Study design and analysis framework, including Sybil's predictive performance across subgroups and comparison with Lung-RADS.

(A) Analytical framework of the study. For the entire study sample (n = 18 057), heavy-smoking subgroup (n = 2848), and never- or light-smoking subgroup (n = 9943), the following evaluations were performed: (a) the ability of Sybil, an open-source deep learning model, to predict lung cancer within 6 years (green box); (b) the predictive performance of Sybil stratified by the visibility of lung cancers on baseline CT scans (blue box); (c) a comparison of sensitivity and specificity of Sybil with those of the Lung CT Screening Reporting and Data System (Lung-RADS) v2022 (gray box); and (d) Sybil's attention maps (red box). Additionally, for the heavy-smoking subgroup, the clinical utility of Sybil in lung cancer screening was investigated by assessing the added value to the U.S. Preventive Services Task Force (USPSTF) recommendation (yellow box). None of the individuals included in this study were previously involved in the development or external testing of Sybil. AUC = area under the receiver operating characteristic curve. (B) Flowchart of the study sample.