Comparative effectiveness of low-dose CT lung cancer screening among high-risk non-smoking female subgroups and males in China
Yumeng Ding, Le Wang, Wanting Ren, Weiwei Gong, Lingbin Du, Xiangdong Cheng

TL;DR
This study compares lung cancer screening effectiveness between non-smoking females and males in China, finding that non-smoking females have lower screening rates and detection rates compared to smoking males, but a subgroup with passive smoking and hormonal issues shows similar outcomes.
Contribution
The study identifies a subgroup of non-smoking females with lung cancer screening outcomes comparable to males, suggesting the need for sex-specific screening criteria.
Findings
Non-smoking females had lower LDCT adherence, positive findings, and detection rates compared to smoking males.
A subgroup of non-smoking females with passive smoking and hormonal abnormalities showed screening outcomes similar to males.
Passive smoking and hormonal abnormalities were independent risk factors for lung cancer in non-smoking females.
Abstract
Lung cancer screening guidelines abroad predominantly rely on smoking pack-years and age, Chinese guidelines uniquely incorporate non-smoking risk factors, though empirical validation remains limited. This study compared low-dose computed tomography (LDCT) screening participation and effectiveness between non-smoking females and males in real-world settings. This study used data from the Cancer Screening Program in Urban China (CanSPUC) in Zhejiang Province, spanning from April 2019 to December 2024. Eligible participants were aged 45 to 74 and were evaluated for a high risk for lung cancer. We stratified the population into three comparison groups: non-smoking females, non-smoking males and smoking males. Four key screening metrics were compared between groups: LDCT adherence rate, positive rate, lung cancer detection rate and false-positive rate. Multivariable robust (modified)…
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Taxonomy
TopicsLung Cancer Diagnosis and Treatment · Lung Cancer Treatments and Mutations · Radiomics and Machine Learning in Medical Imaging
