Actively Recruiting
AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors
Led by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Updated on 2024-02-29
4000
Participants Needed
4
Research Sites
252 weeks
Total Duration
On this page
Sponsors
S
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Lead Sponsor
S
Sun Yat-sen University
Collaborating Sponsor
AI-Summary
What this Trial Is About
Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates. In recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine. The research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading. The project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.
CONDITIONS
Official Title
AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Patients diagnosed with a phyllodes tumor of the breast
You will not qualify if you...
- Blurred images, imaging artifacts
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 4 locations
1
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, China, 510050
Actively Recruiting
2
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
Guangzhou, Guangdong, China, 510120
Actively Recruiting
3
The Third Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, China, 510145
Actively Recruiting
4
Guangdong Maternal and Child Health Hospital
Guangzhou, Guangdong, China, 511400
Actively Recruiting
Research Team
Y
Yan Nie, Prof.Dr.
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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