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

Completed

• Conditions: Breast Cancer

• Registration Number: NCT04909554
• Lead Sponsor: Guangdong Provincial People's Hospital

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 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.

Study Timeline

• Study Start: 2019-01-01
• Study First Submitted: 2021-05-09
• Study First Submitted QC: 2021-05-28
• Study First Posted: 2021-06-01
• Primary Completion: 2023-07-30
• Study Completion: 2023-12-30
• Status Verified: 2024-08
• Last Update Submitted: 2024-08-03
• Last Update Posted: 2024-08-06

Recruitment & Eligibility

• Status: COMPLETED
• Sex: Female
• Target Recruitment: 301

Inclusion Criteria

• Age ≥18 years;
  • Histologically confirmed invasive breast carcinoma;
  • Clinical stage II-III at presentation;
  • Complete basic information and image data;
  • Have MRI imaging data at baseline and after the first cycle of NAC;
  • Finish the standard NAC treatment and undergo surgery;

Exclusion Criteria

• With chemotherapy contraindications;
  • Multifocal of multicentric lesions;
  • Poor quality of MRI images;

2. For validation cohort:

Inclusion Criteria:

• Age ≥18 years;
• Complete basic information and image data;
• Clinical stage II-III at presentation;
• Scheduled for neoadjuvant chemotherapy;
• Eastern Cooperative Oncology Group (ECOG) performance status of 0-1.
• Signed informed consent;

Exclusion Criteria:

• With chemotherapy contraindications;
• Metastatic breast cancer;
• Multifocal of multicentric lesions;

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Sensitivity	up to 28 weeks	Testing the sensitivity of NeoMDSS model to predict pCR using the area under receiver operating characteristic curve.

Secondary Outcome Measures

Name	Time	Method
Specificity	up to 6 weeks	Testing the difference in the tumor shrinkage patterns on magnetic resonance imaging (MRI) in triple-negative breast cancer patients receiving neoadjuvant therapy , as well as the correlation between tumor regression pattern and efficacy.

Trial Locations

Locations (1)

Guangdong Provincial People's Hospital; 🇨🇳 Guangzhou, Guangdong, China