Development of predictive models for pathological response status in breast cancer after neoadjuvant therapy based on
Shuqiang Liu1, Cong Jiang2, Danping Wu1
1Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
Background:
Achieving a pathological complete response (pCR) after neoadjuvant therapy (NAT) is considered to be a critical factor for a favourable prognosis in breast cancer. However, discordant pathological complete response (DpCR), characterised by isolated responses in the breast or axillary, represents an intermediate pathological response category between no response and complete response. This study aims to investigate predictive factors and develop models based on peripheral blood inflammatory indexes to more accurately predict NAT outcomes.
Method:
A total of 789 eligible patients were enrolled in this retrospective study. The patients were randomized into training and validation cohort according to a 7:3 ratio. Lasso and uni/multivariable logistic regression analysis were applied to identify the predictor variables. Two Nomograms combining clinico-pathologic features and peripheral blood inflammatory indexes were developed.
Result:
Molecular Subtype, HALP, P53, and FAR were used to construct the predictive models for traditional non pCR (T-NpCR) and total-pCR (TpCR). The T-NpCR group was divided into DpCR and non pCR (NpCR) subgroups to construct a new model to more accurately predict NAT outcomes. cN, HALP, FAR, Molecular Subtype, and RMC were used to construct the predictive models for NpCR and DpCR. The receiver operating characteristic (ROC) curves indicate that the model exhibits robust predictive capacity. Clinical Impact Curves (CIC) and Decision Curve Analysis (DCA) indicate that the models present a superior clinical utility.
Conclusion:
HALP and FAR were identified as peripheral blood inflammatory index predictors for accurately predicting NAT outcomes.
