ArticleDiscover oncology2025
Conditional survival prediction in elderly patients with hormone receptor positive locally advanced breast cancer using a competing risks model.
Article in Discover oncology, 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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Abstract
purposeConditional survival (CS) analysis reveals dynamic changes in long-term survival, offering cancer survivors more accurate prognosis. We aimed to develop a prognostic model for elderly patients with hormone receptor-positive locally advanced breast cancer (HR+ LABC) based on CS.
methodsData for patients aged > 65 years with HR+ LABC were obtained from the Surveillance, Epidemiology, and End Results database (2010-2021, n = 8,450) and randomly divided into training (n = 5,915, 70%) and validation (n = 2,535, 30%) sets. CS analysis assessed changes in 10-year survival over time. Multivariable Cox regression identified prognostic factors for breast cancer-specific mortality (BCSM, defined as death due to breast cancer) and overall mortality (OM, defined as death from any cause) for model development.
resultsFor the entire cohort, 10-year OM and 10-year BCSM were 58.7% (95% CI 56.8%-60.4%) and 28.0% (95% CI 26.6%-29.4%), respectively. Both OM and BCSM improved over time according to CS analysis. Competing risks regression identified seven independent factors associated with BCSM: age, T stage, N stage, histological grade, HR status, chemotherapy, and radiotherapy. Ten prognostic factors were identified for OM, with additional factors including marital status, race, and surgery (all p < 0.05). The CS models demonstrated strong concordance between predicted and observed outcomes in both training and validation sets. The median time-dependent area under the curve (AUC) for the 10-year period remained above 0.73 for both models.
conclusionsThis study investigated the CS patterns in elderly patients with HR-positive LABC and develop personalized CS prediction models. These models may assist in patient counseling, guide clinical decision-making, and support healthcare resource allocation during follow-up. However, several limitations remain before clinical application, including the absence of important clinical variables, limited applicability to non-surgical patients, and the lack of prospective validation. Future implementation will require external validation and further model refinement.
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