Clinical trial NCT06649565: Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy
Official title: Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Breast Cancer
Lead sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Sponsor class: OTHER
First posted: 2024-10-18
Enrollment: 300
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: This study adopts sequential enrollment (continuously recruiting patients until the sample size requirement is met). The subjects are patients with neoadjuvant treatment for breast cancer who were treated in Beijing Second Hospital, Beijing Luhe Hospital Affiliated to Capital Medical University, and Beijing Sanhuan Cancer Hospital from January 2024 to December 2026. Each independent medical center will include 100 patients, for a total of 300 patients.

Brief summary: 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.

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

Primary outcomes:
measure: Breast MRI radiomics characteristics of breast cancer patients during neoadjuvant therapy
timeFrame: Breast cancer MRI images before neoadjuvant therapy and immediately after completing neoadjuvant therapy
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Sanhuan Cancer Hospital, Chaoyang District, Beijing(Cancer Hospital, Chinese Academy of Medical Sciences, close medical alliance)
state: Beijing
city: Beijing
country: China
facility: Cancer Hospital, Chinese Academy of Medical Sciences
state: Beijing
Study officials:
affiliation: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
name: peng yuan, doctor
role: STUDY_DIRECTOR
References: