NCT04909554

MRI-based Approaches for Multi-parametric Model to Early Predict Pathological Complete Response to Neoadjuvant Therapy in Breast Cancer

MRI-based Approaches for Multi-parametric Model to Early Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer (NeoMDSS)

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 301 (actual)
• Sponsor: Guangdong Provincial People's Hospital · Academic / Other
• Sex: Female
• Age: 18 Years
• Healthy volunteers: Not accepted

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 therapy (NAT) 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. NAT 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 NAT 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 301 patients undergoing MRI after the first cycle of neoadjuvant chemotherapy. This project plans to establish and validate a model for determining pCR during NAT 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. 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.

Conditions

• Breast Cancer

Timeline

• Start date: 2019-01-01
• Primary completion: 2023-07-30
• Completion: 2023-12-30
• First posted: 2021-06-01
• Last updated: 2024-08-06

Locations

1 site across 1 country: China