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. 2025 Jun 18;23(1):238.
doi: 10.1186/s12957-025-03892-1.

Risk factors and clinical risk stratification of distant metastasis in early-stage lung cancer in never smokers

Dongsheng Wu #  1 , Xiaohu Hao #  1 , Zhipeng Gong #  1 , Ruichen Cui  1 , Liang Xia  1 , Lunxu Liu  2
Affiliations

Risk factors and clinical risk stratification of distant metastasis in early-stage lung cancer in never smokers

Dongsheng Wu et al. World J Surg Oncol. .

Abstract

Background: Risk factors for distant metastasis in early-stage lung cancer in never smokers (LCINS) remain poorly understood. This study aimed to identify key risk factors and to develop a clinical risk stratification model for early-stage LCINS.

Methods: We retrospectively analyzed patients diagnosed with early-stage LCINS at West China Hospital, Sichuan University, from 2015 to 2020. Univariable and multivariable Cox regression analyses were performed to identify independent risk factors for distant metastasis. A predictive model was developed and internally validated using bootstrap resampling, with performance assessed by the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis.

Results: A total of 1,406 patients with pathological stage I-II LCINS were included, among whom 76 (5.41%) developed distant metastasis during follow-up. Multivariable Cox regression analysis revealed that independent risk factors included advanced pathological T and N stages, higher consolidation-to-tumor ratio, and histologic subtype, particularly solid/micropapillary predominant adenocarcinoma. Based on these predictors, a predictive model was developed, demonstrating strong discrimination with a C-index of 0.799 and AUC values of 0.809, 0.791, and 0.783 for predicting 1-, 2-, and 3-year distant metastasis, respectively. Calibration and decision curve analyses confirmed the reliability and clinical utility of the model.

Conclusions: This study identified risk factors and developed a clinical risk stratification model for distant metastasis in early-stage LCINS. This validated model enables risk stratification and personalized monitoring to facilitate early detection of distant recurrence in LCINS.

Keywords: Distant metastasis; Lung cancer in never smokers; Predictive model; Risk factors.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This is an observational study. The Institutional Review Board of West China Hospital, Sichuan University has confirmed that no ethical approval is required. Consent for publication: All authors gave consent for the publication of this study. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart illustrating the patient selection process. Abbreviations: NSCLC, non-small cell lung cancer; LCINS, lung cancer in never smokers; C-index, Harrell concordance index; ROC, receiver operating characteristic curves; AUC, area under the curve; DCA, decision curve analysis
Fig. 2
Fig. 2
Nomogram model for predicting distant metastasis in patients with early-stage LCINS. The patient #6 is illustrated in the nomogram by mapping its values to the covariate scales. The probability of distant metastasis in 1-, 2-, and 3-year follow-up are estimated to be 3.16%, 6.27%, and 9.56%, respectively. Abbreviation: LCINS, lung cancer in never smokers
Fig. 3
Fig. 3
A-C Time-dependent ROC curves for predicting the probability of distant metastasis at 1, 2, and 3 years, respectively. D-F Calibration curves showing predicted versus observed probabilities of distant metastasis at 1, 2, and 3 years, respectively. G-I DCA illustrating the clinical utility of the model at 1, 2, and 3 years, respectively
Fig. 4
Fig. 4
Kaplan–Meier curves comparing distant metastasis outcomes between low- and high-risk groups for early-stage LCINS. Abbreviation: LCINS, lung cancer in never smokers
See this image and copyright information in PMC

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