Long-term outcomes of lung cancer screening in males and females

Lung Cancer. 2023 Nov:185:107387. doi: 10.1016/j.lungcan.2023.107387. Epub 2023 Oct 4.

Abstract

Background: This study explored female and male overall mortality and lung cancer (LC) survival in two LC screening (LCS) populations, focusing on the predictive value of coronary artery calcification (CAC) at baseline low-dose computed tomography (LDCT).

Methods: This retrospective study analysed data of 6495 heavy smokers enrolled in the MILD and BioMILD LCS trials between 2005 and 2016. The primary objective of the study was to assess sex differences in all-cause mortality and LC survival. CAC scores were automatically calculated on LDCT images by a validated artificial intelligence (AI) software. Sex differences in 12-year cause-specific mortality rates were stratified by age, pack-years and CAC score.

Results: The study included 2368 females and 4127 males. The 12-year all-cause mortality rates were 4.1 % in females and 7.7 % in males (p < 0.0001), and median CAC score was 8.7 vs. 41 respectively (p < 0.0001). All-cause mortality increased with rising CAC scores (log-rank test, p < 0.0001) for both sexes. Although LC incidence was not different between the two sexes, females had lower rates of 12-year LC mortality (1.0 % vs. 1.9 %, p = 0.0052), and better LC survival from diagnosis (72.3 % vs. 51.7 %; p = 0.0005), with a similar proportion of stage I (58.1 % vs. 51.2 %, p = 0.2782).

Conclusions: Our findings demonstrate that female LCS participants had lower rates of all-cause mortality at 12 years and better LC survival than their male counterparts, with similar LC incidence rates and stage at diagnosis. The lower CAC burden observed in women at all ages might contribute to explain their lower rates of all-cause mortality and better LC survival.

Keywords: All-cause mortality; Artificial intelligence; Coronary artery calcifications; Female lung cancer; Low-dose computed tomography; Lung cancer screening.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Artificial Intelligence
  • Coronary Artery Disease*
  • Early Detection of Cancer / methods
  • Female
  • Humans
  • Lung Neoplasms* / diagnosis
  • Lung Neoplasms* / epidemiology
  • Male
  • Retrospective Studies
  • Risk Factors