A novel formula to improve the accuracy and prognostic ability of determining the survival time after recurrent breast cancer.

Abstract

Background: Recurrent breast cancer (BC) has poor prognosis. To ascertain the survival time after recurrence (STR) is necessary for improving QOL and for selecting appropriate treatment. An investigation was conducted to determine whether certain biomarkers would improve prognosis and whether the disease-free interval (DFI) and biomarkers can predict STR.

Methods: Cases (n = 1,254) with recurrent BC from January 2000 to December 2023 were enrolled in this study. The cases were divided into the 2000-2005 group (n = 182), 2006-2011 group (n = 331), 2012-2017 group (n = 369), and 2018-2023 group (n = 366). A simple linear regression model was used to identify the relationship between STR and DFI.

Results: Survival rates after recurrence significantly increased in cases after 2012. No improvement was observed in cases with a HER2-0 status and a Ki-67 index value < 15%. A multivariate analysis revealed that the Ki-67 index value was a significant factor in the 2000-2005 group. The ER and HER2 status were significant after 2012. The most significant correlation was found between DFI and STR in deceased patients with a Ki-67 index value > 30%, followed by HER2-low, tumor size ≤ 2 cm and ER < 1%. The following formula was developed to predict STR; $\text{STR (mons)}={\alpha }_0+{\beta }_0\bullet \text{DFI}+{\sum }_{i=1}^5{F}_i\left({\alpha }_i,+,{\beta }_i,\bullet ,\text{DFI}\right)$ CONCLUSION: Prognosis after recurrence clearly improved from 2012. In recurrent deceased patients, there was a strong correlation between DFI and STR in cases with a Ki-67 index value > 30% and a HER2-low status. Further studies need to be conducted in clinical settings to verify the accuracy of the predictive formula developed for STR.