Evidence map›Paper›PMID 41039258›Full record

ArticleBMC cancer2025

Development and validation of a dynamic nomogram for predicting brain metastasis in stage III NSCLC patients undergoing definitive chemoradiotherapy.

Xianyan Chen, Xi Xiao, Min Wang, Ting Mei, Ying Huang, Cheng Yi, Youling Gong

Abstract readValidation Study
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Xianyan Chen *Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
Xi Xiao *West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, 610041, China.
Min WangDivision of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
Ting MeiDivision of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
Ying HuangWest China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, 610041, China.
Cheng YiDivision of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Youling GongDivision of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. drgongyouling@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAlthough survival in stage III non-small cell lung cancer (NSCLC) patients receiving concurrent chemoradiotherapy is significantly prolonged, brain metastasis (BM) remains prevalent. This study aims to develop and validate a comprehensive model for predicting BM risk in stage III NSCLC patients, guiding personalized treatment.

methodsA total of 311 patients were retrospectively analyzed and randomly divided into a training cohort (n = 230) and a validation cohort (n = 81). Univariate analysis identified potential predictors, followed by multivariate analysis using stepwise AIC regression to determine independent risk factors. A nomogram model was constructed and validated with ROC curves, calibration curves, and decision curve analysis, which was used for risk stratification.

resultsOf the 311 patients, 45 (14.5%) developed BM. Key independent predictors included sex, EGFR mutation, liver metastasis, immune maintenance deficiency, neuron-specific enolase, carcinoembryonic antigen, and absolute lymphocyte count. The model demonstrated robust predictive performance, with an area under the ROC curve of 0.813 in the training cohort and 0.775 in the validation cohort, along with favorable calibration and decision curve analysis. Kaplan-Meier survival analysis showed that patients with BM had significantly shorter overall survival (43.3 vs. 75.8 months, p = 0.007). Using a nomogram-derived cutoff score of 393.79, patients were stratified into high- and low-risk groups, with the high-risk group exhibiting significantly shorter median overall survival compared to the low-risk group (p = 0.034).

conclusionThis validated nomogram offers a practical tool for early identification of high-risk patients with stage III NSCLC, facilitating personalized surveillance and intervention strategies to improve outcomes.

Indexed as

Brain NeoplasmsCarcinoma, Non-Small-Cell LungChemoradiotherapyLung NeoplasmsNomogramsAdultAgedFemaleHumansKaplan-Meier EstimateMaleMiddle AgedNeoplasm StagingPrognosisRetrospective StudiesROC CurveBrain metastasisChemoradiotherapyEGFR mutationImmune maintenance therapyNon-Small-cell lung cancer

Identifiers

PMID41039258
PMCPMC12492854

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