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AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors

NCT06286267 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 4000

Last updated 2024-02-29

No results posted yet for this study

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.

Conditions

  • Phyllodes Breast Tumor
  • Artificial Intelligence
  • Multiomics
  • Prognostic Cancer Model
  • Diagnosis

Interventions

DIAGNOSTIC_TEST

imaging

Patient medical imaging materials including ultrasound, mammography, CT, MRI

Sponsors & Collaborators

  • Sun Yat-sen University

    collaborator OTHER

  • Peking University Shenzhen Hospital

    collaborator OTHER

  • Guangdong Provincial Maternal and Child Health Hospital

    collaborator OTHER

  • The Third Affiliated Hospital of Guangzhou Medical University

    collaborator OTHER

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

    lead OTHER

Eligibility

Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-03-01
Primary Completion
2027-12-31
Completion
2027-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06286267 on ClinicalTrials.gov