Breast cancer is a relatively heterogeneous disease in the older population. Survival in older breast cancer patients is not only affected by tumor-related factors, but also by geriatric assessment domains. How tumor clinical factors and geriatric factors specifically affect the survival rate of older patients and how to combine these two factors to predict the risk of death in older patients with breast cancer remain clinical questions to be addressed.
We used the Peking Union Medical College Hospital database to identify older patients (≥65 years) who were newly diagnosed with breast cancer between January 2013 and December 2019. Of the 641 eligible patients, we retrospectively analyzed the clinical and geriatric data of 556 patients who formed our study population. The primary outcomes were overall survival (OS) and breast cancer-specific survival (BCSS). Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors and construct a nomogram to predict the 1-, 3-, and 5-year survival rates. The performance of the constructed nomogram was evaluated using calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).
Multivariate Cox regression analysis revealed seven independent prognostic factors associated with OS in older patients with breast cancer: age, tumor stage, lymph node stage, intrinsic molecular subtype, functional status, comorbidities, and psychological state. Nomogram based on these seven factors yielded excellent performance, with area under the ROC curve (AUROC) of 0.748. Similarly, the nomogram for BCSS had an AUROC of 0.760. Moreover, the calibration curve and DCA revealed good predictive accuracy between the actual and predicted probabilities.
Independent prognostic factors for OS and BCSS in older patients with breast cancer in China were determined in our study. A novel nomogram for predicting the 1-, 3-, and 5-year OS and BCSS in this patient population was developed and validated. The nomogram exhibited good accuracy, indicating its potential for clinical decision making and improving outcomes.