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

July 15, 2024 updated by: Yunfang Yu, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

An AI Large Language Model Based on Multi-task and Multimodal Data Fusion Accurately 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.

Study Overview

Status

Enrolling by invitation

Conditions

Detailed Description

This is a multicenter, bidirectional cohort study. Retrospective training cohort, retrospective validation cohort and prospective test cohort were designed.

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 training cohort, and data of patients treated in the South ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University for internal validation cohort; Data on patients treated at external centers between January 1, 2002 and August 31, 2023 were retrospectively collected for external validation cohort. Data on patients admitted to Sun Yat-sen Memorial Hospital at Sun Yat-sen University after January 1, 2024 were prospectively collected for the test cohort. Patient data collected included: pathological images and report texts of breast puncture specimens before neoadjuvant chemotherapy, breast magnetic resonance images and report texts before neoadjuvant chemotherapy, postoperative pathological reports and clinical information, etc.. An artificial intelligence large language model based on multi-task and multi-modal data integration was established to accurately predict the efficacy of neoadjuvant chemotherapy for breast cancer, and its predictive efficacy was tested by retrospective validation cohort and prospective double-blind test cohort. The retrospective cohort of this study was followed up to collect clinical data, magnetic resonance imaging and reports, and surgical pathology reports of patients, etc.. When patients had disease recurrence, the DFS time of patients was recorded, and when patients did not have disease recurrence, the last follow-up time was recorded. Baseline data survey was completed during hospitalization of prospective cohort patients. Pathological reports of breast tumors surgically removed after neoadjuvant chemotherapy were obtained during follow-up, as well as the time of disease recurrence and the time of death of patients. Clinical information such as magnetic resonance imaging and reports were collected during follow-up. Follow-up until the end of the 2-year study.

Study Type

Observational

Enrollment (Estimated)

840

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China
        • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

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.

Description

Inclusion Criteria:

  • 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

Exclusion Criteria:

  • 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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive ability of the model for pCR after neoadjuvant chemotherapy in breast cancer patients
Time Frame: 1 year
receiver operating characteristic curve (ROC curve) were used to evaluate the predictive efficiency of the model
1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive ability of the model for DFS after neoadjuvant chemotherapy in breast cancer patients
Time Frame: 1 year
receiver operating characteristic curve (ROC curve) were used to evaluate the predictive efficiency of the model
1 year

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive ability of the model for neoadjuvant chemotherapy drug sensitivity in breast cancer patients
Time Frame: 1 year
receiver operating characteristic curve (ROC curve) were used to evaluate the predictive efficiency of the model
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

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

Collaborators

First Affiliated Hospital of Jinan University

Investigators

  • Study Chair: Yunfang Yu, Doctor, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
  • Study Director: Herui Yao, Doctor, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
  • Principal Investigator: 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

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

August 1, 2024

Primary Completion (Estimated)

June 1, 2025

Study Completion (Estimated)

January 31, 2026

Study Registration Dates

First Submitted

July 4, 2024

First Submitted That Met QC Criteria

July 15, 2024

First Posted (Actual)

July 19, 2024

Study Record Updates

Last Update Posted (Actual)

July 19, 2024

Last Update Submitted That Met QC Criteria

July 15, 2024

Last Verified

July 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SYSKY-2024-276-01

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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