Trial Title:
AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors
NCT ID:
NCT06286267
Condition:
Phyllodes Breast Tumor
Artificial Intelligence
Multiomics
Prognostic Cancer Model
Diagnosis
Conditions: Official terms:
Breast Neoplasms
Phyllodes Tumor
Disease
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Other
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
imaging
Description:
Patient medical imaging materials including ultrasound, mammography, CT, MRI
Arm group label:
Breast phyllodes tumor
Summary:
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.
Criteria for eligibility:
Study pop:
Patients are all those who attended Sun Yat-Sen Memorial Hospital of Sun Yat-Sen
University.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- Patients diagnosed with a phyllodes tumor of the breast
Exclusion Criteria:
- Blurred images, imaging artifacts
Gender:
Female
Minimum age:
N/A
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Sun Yat-sen University Cancer Center
Address:
City:
Guangzhou
Zip:
510050
Country:
China
Status:
Recruiting
Contact:
Last name:
Feng Ye, Prof.Dr.
Phone:
15914388994
Facility:
Name:
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
Address:
City:
Guangzhou
Zip:
510120
Country:
China
Status:
Recruiting
Contact:
Last name:
Yan Nie, Prof.Dr.
Phone:
+86 020-81332587
Email:
nieyan7@mail.sysu.edu.cn
Facility:
Name:
The Third Affiliated Hospital of Guangzhou Medical University
Address:
City:
Guangzhou
Zip:
510145
Country:
China
Status:
Recruiting
Contact:
Last name:
Hui Mai, Prof.Dr.
Phone:
13925129112
Facility:
Name:
Guangdong Maternal and Child Health Hospital
Address:
City:
Guangzhou
Zip:
511400
Country:
China
Status:
Recruiting
Contact:
Last name:
Yu Tan, Prof.Dr.
Phone:
13632356526
Start date:
March 1, 2023
Completion date:
December 31, 2027
Lead sponsor:
Agency:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Agency class:
Other
Collaborator:
Agency:
Sun Yat-sen University
Agency class:
Other
Collaborator:
Agency:
Peking University Shenzhen Hospital
Agency class:
Other
Collaborator:
Agency:
Guangdong Provincial Maternal and Child Health Hospital
Agency class:
Other
Collaborator:
Agency:
The Third Affiliated Hospital of Guangzhou Medical University
Agency class:
Other
Source:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06286267
