Clinical trial NCT06286267: AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors
Official title: Development of an Artificial Intelligence-Based System for Precise Diagnosis and Prognosis of Breast Phyllodes Tumors
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Phyllodes Breast Tumor
Artificial Intelligence
Multiomics
Prognostic Cancer Model
Diagnosis
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
Collaborators: name: Sun Yat-sen University
name: Peking University Shenzhen Hospital
name: Guangdong Provincial Maternal and Child Health Hospital
name: The Third Affiliated Hospital of Guangzhou Medical University
First posted: 2024-02-29
Enrollment: 4000
Sex: FEMALE
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients are all those who attended Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.

Brief 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.


Eligibility criteria: Inclusion Criteria:

* Patients diagnosed with a phyllodes tumor of the breast

Exclusion Criteria:

* Blurred images, imaging artifacts

Primary outcomes:
measure: Sensitivity
timeFrame: Five years
measure: False-negative Rate
timeFrame: Five years
measure: Specificity
timeFrame: Five years
measure: False-positive Rate
timeFrame: Five years
measure: Receiver Operating Characteristic Curve
timeFrame: Five years
measure: Area under roc Curve
timeFrame: Five years
Secondary outcomes:
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sun Yat-sen University Cancer Center
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
state: Guangdong
city: Guangzhou
country: China
facility: The Third Affiliated Hospital of Guangzhou Medical University
state: Guangdong
city: Guangzhou
country: China
facility: Guangdong Maternal and Child Health Hospital
state: Guangdong
Study officials:
References:
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