Prognostic value of residual cancer burden and Miller-Payne system after neoadjuvant chemotherapy for breast cancer

Background: To verify the feasibility of using the residual cancer burden (RCB) index to stratify prognosis of patients after neoadjuvant chemotherapy (NAC) and to compare RCB with the Miller-Payne system. Methods: We retrospectively analyzed clinicopathological data of patients receiving treatment between January 1, 2010 and December 31, 2018. Kaplan-Meier curves were used to compare the survival outcomes and estimate disease-free survival (DFS) and disease-specific survival (DSS). Harrell's concordance index (C-index) was used to evaluate the predictive accuracy of RCB and Miller-Payne system. Results: A total of 423 female patients with complete data were included in the analysis, with a median follow-up time of 58.5 months (range 7-126). 84 patients experienced recurrence, and 48 experienced breast cancer related death. RCB index and the Miller-Payne system were associated with prognosis in the whole cohort. Patients who achieved RCB-I had similar survival outcomes as those with pathological complete response (pCR, RCB-0). In whole cohort, for the RCB index and the Miller-Payne system, respectively, C-indexes for DFS were 0.73 and 0.64, for DSS were 0.74 and 0.64. The average RCB score was different among three subtypes (F=9.335, P<0.001). Conclusions: The RCB index and the Miller-Payne system can stratify survival outcome of patients after NAC, and RCB had a superior prediction accuracy, especially for triple-negative breast cancer (TNBC). New cut-off value should be sought in order to improve prediction accuracy.

Automated summary

The study retrospectively analyzed clinicopathological data of patients treated between January 1, 2010 and December 31, 2018, finding that RCB index and the Miller-Payne system were associated with prognosis in the whole cohort, with RCB showing superior prediction accuracy, especially for triple-negative breast cancer (TNBC) patients. New cut-off values should be explored to enhance prediction accuracy.