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

NCT06510127 | Enrolling By Invitation

• 1 other identifier: SYSKY-2024-276-01
• Study Type: observational
• Target: 840
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

breast cancer; neoadjuvant chemotherapy; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Predictive ability of the model for pCR after neoadjuvant chemotherapy in breast cancer patients

  receiver operating characteristic curve (ROC curve) were used to evaluate the predictive efficiency of the model

  1 year

Secondary Outcomes (1)

• Predictive ability of the model for DFS after neoadjuvant chemotherapy in breast cancer patients

  1 year

Other Outcomes (1)

• Predictive ability of the model for neoadjuvant chemotherapy drug sensitivity in breast cancer patients

  1 year

Study Arms (4)

training cohort

Data of patients treated in the North Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 1, 2002 to August 31, 2023 were retrospectively collected for the training cohort

internal validation cohort

Data of patients treated in the South Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 1, 2002 to August 31, 2023 were retrospectively collected for the internal validation cohort

external validation cohort

Data on patients treated at external centers between January 1, 2002 and August 31, 2023 were retrospectively collected for the external validation cohort

test cohort

Data on patients admitted to Sun Yat-sen Memorial Hospital of Sun Yat-sen University after January 1, 2024 were prospectively collected for the test cohort

Eligibility Criteria

• Sex: female
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

A total of 840 people will be enrolled in this study, including 300 patients treated in the North Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 200 patients treated in the South Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 170 patients treated in external centers. Prospective collection of data from 170 patients treated at Sun Yat-sen Memorial Hospital.

You may qualify if:

• Women
• Pathological diagnosis of non-metastatic invasive breast cancer (stage II-III)
• At least 4 cycles of neoadjuvant chemotherapy
• Radical surgery was performed after neoadjuvant chemotherapy
• There are pathological images and reports of breast puncture specimens before neoadjuvant chemotherapy
• 'There are MRI images and reports of breast MRI within 2 weeks before neoadjuvant chemotherapy
• There are standard clinical records

You may not qualify if:

• Inflammatory breast cancer
• Bilateral breast cancer
• Newly diagnosed stage IV breast cancer
• Other tumors have not been completely removed or less than 3 years after surgery
• Treatment other than neoadjuvant therapy had been performed before surgery

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead
• First Affiliated Hospital of Jinan University: collaborator

Study Sites (1)

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

Guangzhou, Guangdong, China

Location

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Officials

• Yunfang Yu, Doctor

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

  STUDY CHAIR

• Herui Yao, Doctor

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

  STUDY DIRECTOR

• Kai Chen, Doctor

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

  PRINCIPAL INVESTIGATOR

• Yan Nie, Doctor

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

  PRINCIPAL INVESTIGATOR

• Xiaohui Duan, Doctor

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

  PRINCIPAL INVESTIGATOR

• Jingjing Han, Master

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

  PRINCIPAL INVESTIGATOR

• Yanchun Li, Bachelor

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

  PRINCIPAL INVESTIGATOR

• Wei Ren, Doctor

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

  PRINCIPAL INVESTIGATOR

• Zifan He, Doctor

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

  PRINCIPAL INVESTIGATOR

• Luhui Mao, Bachelor

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

  PRINCIPAL INVESTIGATOR

• Zebang Zhang, Bachelor

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

  PRINCIPAL INVESTIGATOR

• Tang Li, Bachelor

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

  PRINCIPAL INVESTIGATOR

• Zhenjun Huang, Bachelor

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

  PRINCIPAL INVESTIGATOR

• Wei Zhang, Doctor

  First Affiliated Hospital of Jinan University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr.

Study Record Dates

• First Submitted: July 4, 2024
• First Posted: July 19, 2024
• Study Start: August 1, 2024
• Primary Completion: June 1, 2025
• Study Completion: January 31, 2026
• Last Updated: July 19, 2024

Record last verified: 2024-07

Locations

CN (1)