A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer

• DOI: 10.3389/fonc.2024.1323226
• Publication Date: 2024-02-14T05:04:21Z
• Authors: Shan Fang; Wenjie Xia; Haibo Zhang; Chao Ni; Jun Wu; Qiuping Mo; Mengjie Jiang; Dandan Guan; Hongjun Yuan; Wuzhen Chen
• Keywords: Complete response; Neoadjuvant Therapy

This study aimed to develop and validate a clinicopathological model predict pathological complete response (pCR) neoadjuvant chemotherapy (NAC) in breast cancer patients identify key prognostic factors. retrospective analyzed data from 279 who received NAC at Zhejiang Provincial People's Hospital 2011 2021. Additionally, an external validation dataset, comprising 50 Lanxi Second Affiliated Hospital, University School of Medicine 2022 2023 was utilized for verification. A multivariate logistic regression established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), pathology variables baseline post-NAC. Model performance predicting pCR evaluated. Prognostic factors were identified using survival analysis. In the enrolled, pathologic rate 27.96% (78 out 279) achieved. The predictive incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, echo features. demonstrated strong accuracy (C-statistics/AUC 0.874), especially human epidermal growth factor receptor 2 (HER2)-enriched 0.878) triple-negative 0.870) subtypes, performed well set 0.836). Incorporating cell (CTC) changes post-NAC size further improved 0.945) CTC detection subgroup. Key included >5cm, lymph node metastasis, sTIL estrogen (ER) status pCR. Despite varied rates, overall prognosis after standard systemic therapy consistent across subtypes. developed showcases robust forecasting NAC-treated patients, marking step toward more personalized therapeutic strategies cancer.