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

• Published in: Breast cancer (Tokyo, Japan) Vol. 31; no. 5; pp. 955 - 968
• Main Authors: Wei, Jiao-fei, Li, Fan, Lin, Jia-wen, Dou, Zi-ang, Li, Shu-qin, Shen, Jun
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.09.2024
• Subjects: Adult; Aged; Antineoplastic Combined Chemotherapy Protocols - therapeutic use; Biomarkers, Tumor - analysis; Biomarkers, Tumor - metabolism; Breast Neoplasms - drug therapy; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Cancer Research; Chemotherapy, Adjuvant - methods; Female; Humans; Medicine; Medicine & Public Health; Middle Aged; Neoadjuvant Therapy - methods; Oncology; Original Article; Remission Induction; Retrospective Studies; ROC Curve; Surgery; Surgical Oncology; Neoadjuvant chemotherapy; Reg IV; Breast cancer; pCR; Prediction model
• ISSN: 1340-6868; 1880-4233; 1880-4233
• DOI: 10.1007/s12282-024-01609-y

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