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

(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.

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