ArticleDiscover oncology2025
A nomogram for predicting overall survival in young triple-negative breast cancer patients: a population-based study.
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
backgroundYoung triple negative breast cancer (YTNBC) patients often face poor survival outcomes. Given the high-risk nature of YTNBC, there is an urgent need for tools that can accurately predict patient outcomes and guide personalized treatment strategies. Prognostic models, particularly those in the form of nomograms, have gained popularity in oncology for their ability to integrate multiple clinical variables to estimate individual patient survival. Our study aimed to investigate independent prognostic factors in YTNBC patients and develop a nomogram to predict OS, thereby helping patients choose a better therapeutic approach.
methodsPatients diagnosed with YTNBC between January 2010 and December 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into training and validation cohorts at a ratio of 7:3. Univariate and multivariate Cox analyses were conducted to identify significant factors associated with prognosis, which were then used to construct a nomogram for predicting 1-, 3-, and 5-year OS.
resultsNine survival predictors (marital status, tumor grade, AJCC stage, T stage, N stage, M stage, surgery, bone metastases, brain metastases) were selected for nomogram construction. The concordance indexes (C-index), in the training and validation cohorts were 0.749 and 0.745, respectively. The nomogram model demonstrated good calibration, and time-dependent receiver operating characteristic (ROC) curves confirmed its superiority for clinical utility. Additionally, Kaplan-Meier survival curves of various independent prognostic factors validated the model.
conclusionsThe novel nomogram serves as a reliable tool for predicting survival, aiding clinicians in identifying high-risk patients and devising individualized treatments.
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