Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy

Chinese Academy of Medical Sciences & Peking Union Medical College

Status: Enrolling

Conditions: Breast Cancer

Treatments: Other: no intervention

Study type: Observational

Funder types: Other

Identifiers

NCT06649565

2024-1- 4021

Details and patient eligibility

About

Breast cancer has become the world's number one cancer. While its therapeutic efficacy is increasing, how to achieve non-invasive evaluation of the efficacy of neoadjuvant therapy (NAT) for breast cancer patients and thus avoid surgery has become a bottleneck problem that needs to be broken through in clinical diagnosis and treatment. Existing non-invasive evaluation strategies are limited to single-center, single-modality modeling, and have problems such as low performance and poor versatility. Therefore, in the early stage of this study, multi-modality breast cancer patient data from multiple centers across the country were collected and the establishment of an artificial intelligence (AI) efficacy prediction model was preliminarily completed. On this basis, this project intends to further improve the multi-center prospective validation study of the prediction model. The research results will help solve the scientific problem of non-invasive judgment of NAT efficacy in breast cancer patients and provide a new paradigm for the research of high-performance AI diagnosis and treatment auxiliary systems applicable to multiple centers.

Full description

(1) Prospectively collect breast MRI original images (DCE and ADC sequences) and corresponding clinical and surgical pathological data of multi-center breast cancer patients before and after neoadjuvant treatment, store and transport them via mobile hard disks, and input the processed data into the established efficacy determination model stored in a dedicated cloud server; (2) Use artificial intelligence to automatically delineate the ROI area and extract the imaging genomics and deep learning features therein, and combine the clinical pathological characteristics of the patients to further prospectively verify the effectiveness of the established pCR efficacy determination model.

Enrollment: 300 estimated patients

Sex: Female

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients who were treated in the above research centers between January 1, 2024 and October 31, 2025;
• ≥18 years old, female, ECOG score ≤2;
• Pathological biopsy confirmed invasive breast cancer;
• AJCC (8th edition) stage I-III;
• MRI imaging data before and after neoadjuvant therapy;
• Planned mastectomy or breast-conserving surgery after neoadjuvant therapy, and postoperative pathological information obtained.

Exclusion criteria

• Bilateral breast cancer, multiple lesions, or occult breast cancer;
• Poor MRI data quality;
• Patients who had received other anti-tumor treatments before enrollment;
• Patients with other malignant tumors

Trial design

300 participants in 2 patient groups

Breast cancer patients who achieved pathological complete response after neoadjuvant therapy

Description:

All enrolled breast cancer patients received normal neoadjuvant therapy and subsequent surgery without intervention in the diagnosis and treatment process. They were judged to have achieved pathological complete respone based on surgical pathology.

Treatment:

Other: no intervention

Breast cancer patients who did not achieve pathological complete response after neoadjuvant therapy

Description:

All enrolled breast cancer patients received normal neoadjuvant therapy and subsequent surgery without intervention in the diagnosis and treatment process. They were judged as not achieving pathological complete respone based on surgical pathology.

Treatment:

Other: no intervention