A clinical calculator to predict disease outcomes in women with triple-negative breast cancer

Purpose Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, characterized by substantial risks of early disease recurrence and mortality. We constructed and validated clinical calculators for predicting recurrence-free survival (RFS) and overall survival (OS) for TNBC. Methods Data from 605 women with centrally confirmed TNBC who underwent primary breast cancer surgery at Mayo Clinic during 1985-2012 were used to train risk models. Variables included age, menopausal status, tumor size, nodal status, Nottingham grade, surgery type, adjuvant radiation therapy, adjuvant chemotherapy, Ki67, stromal tumor-infiltrating lymphocytes (sTIL) score, and neutrophil-to-lymphocyte ratio (NLR). Final models were internally validated for calibration and discrimination using ten-fold cross-validation and compared with their base-model counterparts which include only tumor size and nodal status. Independent external validation was performed using data from 478 patients diagnosed with stage II/III invasive TNBC during 1986-1992 in the British Columbia Breast Cancer Outcomes Unit database. Results Final RFS and OS models were well calibrated and associated with C-indices of 0.72 and 0.73, as compared with 0.64 and 0.62 of the base models (p < 0.001). In external validation, the discriminant ability of the final models was comparable to the base models (C-index: 0.59-0.61). The RFS model demonstrated greater accuracy than the base model both overall and within patient subgroups, but the advantages of the OS model were less profound. Conclusions This TNBC clinical calculator can be used to predict patient outcomes and may aid physician's communication with TNBC patients regarding their long-term disease outlook and planning treatment strategies.

Automated summary

Clinical calculators for predicting recurrence-free survival and overall survival in triple-negative breast cancer patients were constructed and validated, with higher accuracy and predictive ability than base models.