Conditional survival nomogram for dynamic prognostic prediction in older women with surgically treated breast cancer.

Authors

Haolong Niu, Jinying Zhang

Published in

Biomolecules & biomedicine. Jul 24, 2026. Epub Jul 24, 2026.

Abstract

Prognostic estimates established at breast cancer diagnosis may become less informative as patients survive longer, particularly among older women with substantial competing mortality risks. This study aimed to develop and internally validate a dynamic conditional survival (CS) nomogram for predicting breast cancer-specific survival (BCSS) in surgically treated women aged ≥70 years. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database for patients diagnosed between 2010 and 2015. The 44,354 eligible patients were randomly assigned to training (n = 31,047) and internal validation (n = 13,307) cohorts. A random survival forest (RSF) algorithm was used to select prognostic variables, which were incorporated into a multivariable Cox regression model to construct a nomogram combining conventional and conditional BCSS estimates. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic curves, calibration analysis, and decision curve analysis. The RSF algorithm selected age, T stage, N stage, M stage, histological grade, and molecular subtype as independent predictors of BCSS. The estimated probability of surviving to 10 years increased from 88% at diagnosis to 89%, 92%, 95%, and 97% after surviving 1, 3, 5, and 7 years, respectively. The model achieved C-indices of 0.833 and 0.830 in the training and validation cohorts, respectively, with time-dependent areas under the curve ranging from 0.841 to 0.882. Calibration was satisfactory, and the nomogram provided greater net benefit than tumor-node-metastasis staging alone. This internally validated nomogram provides individualized, time-updated BCSS estimates and may support dynamic risk stratification during long-term follow-up, although external validation is required before clinical implementation.

PMID:
42496101
Bibliographic data and abstract were imported from PubMed on 24 Jul 2026.

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