Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: no intervention.
- Who it may be relevant to
- Registry conditions: Breast Cancer. Basic parameters: from 18 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Overview
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.
Detailed description
(1) Prospectively collect breast MRI original images (DCE and ADC sequences) and corresponding clinical and surgical pathological data of multi-center breast cancer patients before and after neoadjuvant treatment, store and transport them via mobile hard disks, and input the processed data into the established efficacy determination model stored in a dedicated cloud server; (2) Use artificial intelligence to automatically delineate the ROI area and extract the imaging genomics and deep learning features therein, and combine the clinical pathological characteristics of the patients to further prospectively verify the effectiveness of the established pCR efficacy determination model.
Interventions
- Other no intervention
no intervention
Primary outcome measures
- Breast MRI radiomics characteristics of breast cancer patients during neoadjuvant therapy [Time frame: Breast cancer MRI images before neoadjuvant therapy and immediately after completing neoadjuvant therapy]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 2 centers
- Sanhuan Cancer Hospital, Chaoyang District, Beijing(Cancer Hospital, Chinese Academy of Me — Beijing
- Cancer Hospital, Chinese Academy of Medical Sciences — Beijing
Identifiers
NCT: NCT06649565 · 2024-1- 4021