Role of sex in lung cancer risk prediction based on single low-dose chest computed tomography

• Published in: Scientific reports Vol. 13; no. 1; p. 18611
• Main Authors: Simon, Judit, Mikhael, Peter, Tahir, Ismail, Graur, Alexander, Ringer, Stefan, Fata, Amanda, Jeffrey, Yang Chi-Fu, Shepard, Jo-Anne, Jacobson, Francine, Barzilay, Regina, Sequist, Lecia V., Pace, Lydia E., Fintelmann, Florian J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.10.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1612; 631/67/2322; Algorithms; Artificial intelligence; Cancer screening; Chest; Computed tomography; Females; Health risks; Humanities and Social Sciences; Lung cancer; Males; Medical screening; multidisciplinary; Science; Science (multidisciplinary); Sex differences; Tomography
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-45671-6

A validated open-source deep-learning algorithm called Sybil can accurately predict long-term lung cancer risk from a single low-dose chest computed tomography (LDCT). However, Sybil was trained on a majority-male cohort. Use of artificial intelligence algorithms trained on imbalanced cohorts may lead to inequitable outcomes in real-world settings. We aimed to study whether Sybil predicts lung cancer risk equally regardless of sex. We analyzed 10,573 LDCTs from 6127 consecutive lung cancer screening participants across a health system between 2015 and 2021. Sybil achieved AUCs of 0.89 (95% CI: 0.85–0.93) for females and 0.89 (95% CI: 0.85–0.94) for males at 1 year, p = 0.92. At 6 years, the AUC was 0.87 (95% CI: 0.83–0.93) for females and 0.79 (95% CI: 0.72–0.86) for males, p = 0.01. In conclusion, Sybil can accurately predict future lung cancer risk in females and males in a real-world setting and performs better in females than in males for predicting 6-year lung cancer risk.