Spots Global Cancer Trial Database for MRI-based Approaches for Multi-parametric Model to Early Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

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Trial Identification

Brief Title: MRI-based Approaches for Multi-parametric Model to Early Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Official Title: An MRI-based, Multi-parametric Model for Early Prediction of Pathological Complete Response After the First Cycle of Neoadjuvant Chemotherapy in Breast Cancer: a Multicenter, Prospective Cohort Study

Study ID: NCT04909554

Conditions

Breast Cancer

Interventions

Study Description

Brief Summary: The purpose of this clinical research is to evaluate the accuracy of a multi-parametric model based on magnetic resonance imaging (MRI) in predicting pathological complete response (pCR) after the first cycle of neoadjuvant chemotherapy (NAC) given to patients with locally advanced breast cancer, thus allowing early chemotherapy regimen modification to increase number of patients achieving pCR or save patients from toxic effects of ineffective chemotherapy.

Detailed Description: Breast cancer is the most prevalent cancer among women worldwide. NAC has been well established in managing breast cancer for patients with locally advanced cancer and early-stage operable breast cancers of specific molecular subtypes. Though pCR has been demonstrated to be associated with better survival, it can only be judged by pathological testing of surgically resected specimens. Thus, predicting pCR earlier during NAC is imperative and can timely switch to a new personalized treatment strategy and exempt from unnecessary chemotherapy toxicity for patients. This is a multicenter, prospective cohort study of 200 patients undergoing MRI after the first cycle of neoadjuvant chemotherapy. This project plans to establish and validate a model for determining pCR during NAC in breast cancer based on clinical information, imaging and pathological information of patients in multiple centers, in order to provide important references for further early diagnosis and personalized treatment. 1. Retrospectively collecting MRI images data, clinical and pathological information, treatment regimens, and curative effect information to build an MRI-based, multi-parametric model. 2. Evaluating the performance of model through internal and external validation cohort by using the receiver operating characteristic (ROC) curve, the area under the curve (AUC), discrimination and calibration measures.