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

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.

Sex

Female

Minimum age

18 Years

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