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

Judit Simon, Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
Peter Mikhael, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Ismail Tahir, Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.Follow
Alexander Graur, Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.Follow
Stefan Ringer, Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.Follow
Amanda Fata, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Yang Chi-Fu Jeffrey, Harvard Medical School, Boston, MA, USA.
Jo-Anne Shepard, Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
Francine Jacobson, Harvard Medical School, Boston, MA, USA.
Regina Barzilay, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Lecia V. Sequist, Harvard Medical School, Boston, MA, USA.
Lydia E. Pace, Harvard Medical School, Boston, MA, USA.
Florian J. Fintelmann, Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. fintelmann@mgh.harvard.edu.Follow

Document Type

Article

Publication Title

Scientific reports

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.

First Page

18611

DOI

10.1038/s41598-023-45671-6

Publication Date

10-30-2023

Identifier

37903855 (pubmed); PMC10616081 (pmc); 10.1038/s41598-023-45671-6 (doi); 10.1038/s41598-023-45671-6 (pii)

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