An AI Model Predicts the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: a Multicenter, Bidirectional Cohort Study

Neoadjuvant chemotherapy is an important part of the systematic treatment of breast cancer, and it is of great clinical significance to predict the efficacy of neoadjuvant chemotherapy in early stage. The emergence of multi-modal artificial intelligence model has brought new ideas for it. However, the limited ability of artificial intelligence to integrate multi-modal data, the lack of multi-modal models, and the insufficient level of evidence in clinical promotion of artificial intelligence are all scientific problems that need to be solved. In the early stage of the study, a variety of artificial intelligence accurate prediction and auxiliary diagnosis and treatment models for breast cancer were constructed based on magnetic resonance imaging and pathomics, etc., and the effectiveness of the models in predicting the curative effect of neoadjuvant chemotherapy for breast cancer was explored. In order to further improve the predictive efficiency of the model and fill the gap in the systematic study of multi-modal data fusion model, this clinical study intends to combine pathological images, magnetic resonance imaging, diagnostic report text and clinical variables to establish an artificial intelligence large language model based on multi-task and multi-modal data fusion to accurately predict the efficacy of neoadjuvant chemotherapy for breast cancer. A multicenter, bidirectional cohort study was conducted to explore the predictive effectiveness of the model.

NCT Number	NCT06510127
Lead Sponsor	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University Collaborators: First Affiliated Hospital of Jinan University
Conditions	Breast Cancer
Enrollment	840 participants
Start Date	2024-08-01
Primary Completion	2025-06-01 (estimated)
Study Completion	2026-01-31 (estimated)
Updated on ClinicalTrials.gov	2024-07-19