Development and internal validation of a predictive model of overall and progression-free survival in eribulin-treated patients with breast cancer based on baseline peripheral blood parameters.

Background: Immune and inflammatory blood parameters have been reported as biomarkers for treatment efficacy. This study aimed to establish a predictive model that includes blood parameters for patients with metastatic breast cancer treated with eribulin.

Methods: A total of 297 patients were enrolled, and their baseline neutrophil-to-lymphocyte ratio, absolute lymphocyte count (ALC), platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), lymphocyte-to-monocyte ratio (LMR), lactate dehydrogenase (LDH), C-reactive protein (CRP), and clinical data were retrospectively collected.

Results: We constructed nomograms to predict overall survival (OS) and progression-free survival (PFS) using blood parameters, including clinical factors. For OS, menopausal status, hormone receptor status, HER2 status, de novo or recurrent, metastatic site, treatment line, ALC, PLR, PNI, LMR, LDH, and CRP were selected to predict the model. We used menopausal status, hormone receptor status, HER2 status, treatment line, PLR, LMR, LDH, and CRP to predict PFS. Both the OS and PFS of patients according to the risk scores were significantly different (p < 0.001). The optimism-corrected C-indices of the nomograms for OS and PFS were 0.680 and 0.622, respectively. The mean time-dependent area under the receiver operating curve values for OS at 1, 2, and 3 years were 0.752, 0.761, and 0.784, respectively, and for PFS at 3, 6, and 12 months were 0.660, 0.661, and 0.650, respectively.

Conclusion: Nomograms incorporating peripheral blood parameters may improve the accuracy of predicting OS and PFS in patients treated with eribulin. Our prediction model may help decision-making for breast cancer patients who are considering eribulin treatment.