# Development and validation of a neoadjuvant chemotherapy pathological complete remission model based on Reg IV expression in breast cancer tissues: a clinical retrospective study

## TL;DR

This study creates a model to predict successful chemotherapy outcomes in breast cancer patients based on Reg IV protein levels and other factors.

## Contribution

A novel predictive model for neoadjuvant chemotherapy response in breast cancer using Reg IV expression and clinical variables.

## Key findings

- Model 2 achieved an AUC of 0.837 in training and 0.897 in testing datasets.
- Variables like HER-2, ER, T-stage, Reg IV, and Treatment were key in the optimal model.
- Decision curve analysis confirmed the model's potential for clinical guidance.

## Abstract

To develop and authenticate a neoadjuvant chemotherapy (NACT) pathological complete remission (pCR) model based on the expression of Reg IV within breast cancer tissues with the objective to provide clinical guidance for precise interventions.

Data relating to 104 patients undergoing NACT were collected. Variables derived from clinical information and pathological characteristics of patients were screened through logistic regression, random forest, and Xgboost methods to formulate predictive models. The validation and comparative assessment of these models were conducted to identify the optimal model, which was then visualized and tested.

Following the screening of variables and the establishment of multiple models based on these variables, comparative analyses were conducted using receiver operating characteristic (ROC) curves, calibration curves, as well as net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Model 2 emerged as the most optimal, incorporating variables such as HER-2, ER, T-stage, Reg IV, and Treatment, among others. The area under the ROC curve (AUC) for Model 2 in the training dataset and test dataset was 0.837 (0.734–0.941) and 0.897 (0.775–1.00), respectively. Decision curve analysis (DCA) and clinical impact curve (CIC) further underscored the potential applications of the model in guiding clinical interventions for patients.

The prediction of NACT pCR efficacy based on the expression of Reg IV in breast cancer tissue appears feasible; however, it requires further validation.

The online version contains supplementary material available at 10.1007/s12282-024-01609-y.