Judit Simon, MD, PhD
Massachusetts General Hospital
Radiology, Thoracic Imaging and Intervention
Judit Simon MD PhD, Peter Mikhael BSc, Ismail Tahir MB BCh BAO, Alexander Graur Cand Med, Amanda Fata BA, Jo-Anne Shepard MD, Francine Jacobson MD PhD, Regina Brazilay PhD, Lecia V Sequist MD MPH, Lydia E Pace MD, Florian J Fintelmann MD
Florian J Fintelmann, MD
A validated open access deep learning algorithm called Sybil can accurately predict long-term lung cancer risk from a single low-dose chest computed tomography (LDCT) scan and its accuracy exceeds clinical risk assessment. Sybil was trained on predominantly (60%) men and use of artificial intelligence algorithms trained on imbalanced cohorts may lead to inequitable outcomes in real-world settings.
We aimed to study whether Sybil works equally well in both sexes.
We included participants who underwent lung cancer screening LDCT at Brigham and Women’s Hospital and Massachusetts General Hospital between 2014 and 2019. Patients without follow-up were excluded. Patients diagnosed with lung cancer according to the institutional cancer registry within 6 years after the baseline LDCT were considered confirmed lung cancers. Those without a lung cancer diagnosis in the cancer registry and one or more negative follow-up LDCT were considered as negative for lung cancer. Area under the curve (AUC) values for women and men were compared with the DeLong-test.
After exclusion, 10,588 LDCTs from 6,141 patients (47.1% women, mean age 64.9±6.2) were analyzed. Sybil achieved AUCs of 0.89 (95%CI: 0.85-0.93) for women and 0.89 (95%CI: 0.85-0.94) for men at 1 year, 0.85 (95%CI: 0.80-0.90) for women and 0.82 (95%CI: 0.77-0.88) for men at 2 years, 0.83 (95%CI: 0.78-0.88) for women and 0.81 (95%CI: 0.76-0.87) for men at 3 years, 0.83 (95%CI: 0.78-0.88) for women and 0.80 (95%CI: 0.75-0.86) for men at 4 years and 0.84 (95%CI: 0.79-0.89) for women and 0.78 (95%CI: 0.72-0.84) for men at 5 years; all p>0.05. At 6 years, AUC was 0.87 (95%CI: 0.83-0.93) for women and 0.79 (95%CI: 0.72-0.86) for men, p=0.009.
Sybil can accurately predict future lung cancer risk in women and men. For predicting long-term lung cancer risk at 6 years, Sybil performs better in women than in men.”