Evidence map›Paper›PMID 42445437›Full record

ArticleTranslational cancer research2026

A LASSO-based nomogram for predicting breast cancer-specific survival in elderly patients after surgery: a large retrospective cohort study.

Ziqiang Wang, Haojie Zhang, Qianqian Li

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Article in Translational cancer research, 2026. 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

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3 authors.

Ziqiang WangDepartment of Breast Surgery, Shandong Medical and Pharmaceutical University Hospital, Binzhou, China.ORCID https://orcid.org/0009-0006-9891-8910
Haojie ZhangDepartment of Thyroid Surgery, Shandong Medical and Pharmaceutical University Hospital, Binzhou, China.ORCID https://orcid.org/0009-0000-1100-8663
Qianqian LiDepartment of Dermatology, Shandong Medical and Pharmaceutical University Hospital, Binzhou, China.ORCID https://orcid.org/0009-0007-7026-8239

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accurate prognostic assessment for elderly patients with breast cancer remains challenging, as the conventional tumor-node-metastasis (TNM) staging system is limited in capturing individual heterogeneity. This study aimed to develop and internally validate a least absolute shrinkage and selection operator (LASSO)-Cox-based nomogram for predicting breast cancer-specific survival (BCSS) in elderly patients. Methods: A total of 69,137 eligible patients diagnosed between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (70%) and validation (30%) cohorts. LASSO regression was applied for variable selection, followed by multivariable Cox proportional hazards modeling to construct a nomogram. Model performance was evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare predictive accuracy with the TNM staging system. Results: 11 variables were identified by LASSO selection and entered into the multivariable Cox model, including surgery, age, tumor grade, TNM stage, radiotherapy, hormone receptor status, primary site and marital status. The nomogram demonstrated acceptable discrimination (training C-index: 0.807) and satisfactory calibration. DCA suggested a higher net clinical benefit of the nomogram compared with TNM staging across a range of threshold probabilities. Conclusions: This LASSO-Cox-based nomogram provides a prognostic model for estimating BCSS in elderly patients and may assist postoperative risk stratification in elderly patients.

Indexed as

Breast cancerelderlyleast absolute shrinkage and selection operator (LASSO)nomogramSurveillance, Epidemiology, and End Results (SEER)

Identifiers

PMID42445437
PMCPMC13357100

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