Table 2:

Performance of Sybil, an Open-Source Deep Learning Model, in Predicting Lung Cancer from Low-Dose Chest CT Scans

Subgroup	1-year Risk	2-year Risk	3-year Risk	4-year Risk	5-year Risk	6-year Risk	Uno C-Index
Entire study sample (n = 18 057)	0.91 (0.83, 1.00)	0.87 (0.79, 0.95)	0.86 (0.81, 0.92)	0.79 (0.72, 0.85)	0.76 (0.70, 0.82)	0.74 (0.68, 0.79)	0.73 (0.67, 0.79)
History of heavy smoking eligible for the USPSTF recommendation (n = 2848)*	0.94 (0.93, 0.95)	0.75 (0.50, 1.00)	0.81 (0.69, 0.93)	0.74 (0.62, 0.86)	0.70 (0.59, 0.82)	0.73 (0.63, 0.83)	0.72 (0.62, 0.83)
History of never- or light-smoking ineligible for the USPSTF recommendation (n = 9943)*	0.90 (0.78, 1.00)	0.90 (0.79, 1.00)	0.87 (0.77, 0.96)	0.77 (0.65, 0.89)	0.74 (0.63, 0.85)	0.71 (0.62, 0.79)	0.70 (0.61, 0.80)

Note.—Unless otherwise specified, data are time-dependent areas under the receiver operating characteristic curves. Data in parentheses are 95% CIs. Sybil demonstrated good performance in external testing among Asian individuals undergoing low-dose CT for health checkups, including the entire study sample and subgroups with heavy smoking history or never- or light-smoking history. Smoking histories for 5266 of 18 057 individuals (29.2%) could not be categorized due to missing pack-year smoking information. The Sybil lung risk score is a deep learning–based risk score for predicting future lung risk within 6 years from a low-dose chest CT examination. USPSTF = U.S. Preventive Services Task force.

^*The USPSTF recommends lung cancer screening for individuals aged 50–80 years who have a 20-pack-year smoking history. Individuals with heavy smoking history were defined as those with at least 20 pack-years of smoking, including those who currently smoke and those who had quit within the past 15 years. All other individuals who ever smoked but did not meet these criteria were classified as having light smoking history (median, 2 pack-years in this study). Individuals with a never-smoking history were defined as those who had never smoked.