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

Autor: Judit Simon, Peter Mikhael, Ismail Tahir, Alexander Graur, Stefan Ringer, Amanda Fata, Yang Chi-Fu Jeffrey, Jo-Anne Shepard, Francine Jacobson, Regina Barzilay, Lecia V. Sequist, Lydia E. Pace, Florian J. Fintelmann
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-023-45671-6
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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