Development and validation of a prognostic nomogram based on the clinical indicators for breast cancer

Background: Breast cancer (BC) is one of the most prevalent malignancies among women worldwide, with heterogeneous outcomes necessitating individualized prognostic assessment. Existing models often rely on specialized biomarkers, limiting their accessibility in routine practice. This study aimed to develop and internally validate a prognostic nomogram based on readily available clinical indicators to predict overall survival (OS) in BC patients. Methods: In this retrospective study, 217 BC patients diagnosed between 2012 and 2015 at The First Affiliated Hospital of Soochow University and The 904th Hospital of the Joint Logistics Support Force of the People's Liberation Army were enrolled. The cohort was randomly split into training and internal validation sets. Prognostic factors were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by univariate and multivariate Cox regression analyses. A nomogram was constructed based on significant predictors from the training set. Its discriminative ability and calibration were evaluated using Harrell's concordance index (C-index), calibration curves, and time-dependent area under the curve (AUC). Results: Six clinical indicators-hypertension, American Joint Committee on Cancer (AJCC) stage, metastasis, Ki-67 status, endocrine therapy, and red blood cell (RBC) count-were identified as independent prognostic factors and incorporated into the nomogram. The model demonstrated excellent discrimination, with a C-index of 0.898. In the training cohort, the AUCs for predicting 1-, 3-, and 5-year OS were 0.93, 0.89, and 0.93, respectively; corresponding values in the validation cohort were 0.98, 0.86, and 0.85. Calibration curves indicated good agreement between predicted and observed survival probabilities. Conclusions: We developed and validated a novel nomogram based on clinical indicators for predicting OS for BC, which showed good application prospect. This model has the potential to help in clinical decision-making and evaluating patient outcomes.