Development and validation of a predictive model for recurrence in postoperative patients with stage ⅠA1-ⅢA non-small cell lung cancer

Sep 30, 2026

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Non-small cell lung cancer (NSCLC) patients face a substantial postoperative risk of recurrence, yet comprehensive predictive models integrating genetic and multifaceted clinical information remain limited. A retrospective cohort of 911 stage ⅠA1-ⅢA NSCLC patients (West China Hospital, Nov 2013–Aug 2020) was used to develop a multivariable prediction model with demographic, clinical, pathological, radiological, and genetic data. Lasso and multivariate Cox analyses identified key risk factors, which were assembled into a nomogram and evaluated with calibration, decision-curve analysis, and ROC performance in training and validation sets, demonstrating accurate recurrence risk stratification for personalized follow-up.

LanguageEnglish

CategoryResearch & Report

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Pages13

Reading Time33 min

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Abstract

Background

Methods

Results

Conclusions

Introduction

Li etal. BMC Cancer (2026) 26:29 BMC Cancer [https://doi.org/10.1186/s12885-025-15275-6](https://doi.org/10.1186/s12885-025-15275-6) RESEARCH Open Access Development and validation of a predictive model for recurrence in postoperative patients with stage ⅠA1-ⅢA non-small cell lung cancer Yi Li 1†, Renjie Xu 1†, Jinghong Xian 1,2,3,4, Zhoufeng Wang 1,2,3, Wang Chen5,6* and Weimin Li 1,2,3,4* Abstract Background Patients of non-small cell lung cancer (NSCLC) face a high risk of recurrence postoperatively, yet there is a lack of comprehensive predictive models that integrate genetic and other multifaceted information. Methods This retrospective cohort study analyzed 911 patients with stage ⅠA1-ⅢA NSCLC in West China Hospital between November 2013 and August 2020, aimed to develop a prediction model incorporating demographic, clinical, pathological, radiological, and genetic data to enhance postoperative risk stratification and inform personalized follow-up and treatment strategies. After Lasso regression and multivariate Cox proportional hazards regression, mutations in JAK1 and STK11, disease stage, visceral pleural invasion (VPI), lymphovascular invasion (LVI), tumor spread through air spaces (STAS), radiological density, cavitary sign, and smoking index (SI), were identified as significant risk factors. Results These variables were integrated into a nomogram model to classify patients into three risk categories for recurrence: low (total score ≤ 100), moderate (100 < total score ≤ 175. 16), and high (total score > 175. 16) . The performance of the nomogram was rigorously assessed through calibration curves, and decision curve analysis (DCA) and receiver operating characteristic (ROC) curve analysis both in training set [AUC:0 . 88, 95% CI: 0.83–0.93 1year; AUC: 0. 85, 95% CI: 0 .81-0. 89r 3year, AUC: 0 . 85, 95% CI: 0 .81-0. 895year] and validation set (AUC: 0 . 85, 95% CI: 0.79–0.92 1year; AUC: 0 . 83, 95% CI: 0 .76–0. 89 3year, AUC: 0 . 84, 95% CI: 0.78–0.91 5year) . Conclusions Our multi-source imaging–genomic–clinical model accurately predicts the risk of recurrence after surgery in stage ⅠA1-ⅢA NSCLC patients and can be used for risk stratification to guide clinical follow-up management and postoperative treatment strategies. Keywords Non-small cell lung cancer, Predictive model, Recurrence, Postoperative, Nomogram †Yi Li and Renjie Xu co-first authors. *Correspondence: Wang Chen [wangchen@pumc.edu.cn](wangchen@pumc.edu.cn)[ ](wangchen@pumc.edu.cn)Weimin Li [weimi003@scu.edu.cn](weimi003@scu.edu.cn) Full list of author information is available at the end of the article © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Introduction Lung cancer remains a leading cause of cancer incidence and mortality worldwide, contributing significantly to the global disease burden [1–3]. In China, lung cancer exhibits the highest incidence and mortality rates among all malignant tumors, with

FAQs

What is the main goal of the study?

To develop and validate a predictive model for recurrence after surgery in stage ⅠA1-ⅢA NSCLC patients by integrating clinical, radiological, and genetic information.

Which patient data were used to build the prediction model?

The model incorporated demographic, clinical, pathological, radiological, and genetic data from 911 retrospective patients.

How was the model validated and evaluated?

The nomogram’s performance was assessed using calibration curves, decision curve analysis (DCA), and ROC analysis in both training and validation sets.

Sep 30, 2026

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