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

Summary

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.

Conditions

• Breast Cancer

Sponsors & Collaborators

• First Affiliated Hospital of Jinan University

  collaborator OTHER

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

  lead OTHER

Principal Investigators

• Yunfang Yu, Doctor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Herui Yao, Doctor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Kai Chen, Doctor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Yan Nie, Doctor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Xiaohui Duan, Doctor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Jingjing Han, Master · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Yanchun Li, Bachelor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Wei Ren, Doctor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Zifan He, Doctor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Luhui Mao, Bachelor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Zebang Zhang, Bachelor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Tang Li, Bachelor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Zhenjun Huang, Bachelor · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Wei Zhang, Doctor · First Affiliated Hospital of Jinan University

Eligibility

Sex

FEMALE

Healthy Volunteers

No

Timeline & Regulatory

Start

2024-08-01

Primary Completion

2025-06-01

Completion

2026-01-31

Countries

• China

Study Locations