Artificial Intelligence, Diagnosis, Multiomics, Phyllodes Breast Tumor, Prognostic Cancer Model
Conditions
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.
Interventions
DIAGNOSTIC_TESTimaging
Patient medical imaging materials including ultrasound, mammography, CT, MRI
Sponsors
Sun Yat-sen University
Peking University Shenzhen Hospital
Guangdong Provincial Maternal and Child Health Hospital
The Third Affiliated Hospital of Guangzhou Medical University
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
FEMALE
Healthy volunteers
No
Inclusion criteria
* Patients diagnosed with a phyllodes tumor of the breast
Exclusion criteria
* Blurred images, imaging artifacts
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | Five years | The probability of a positive test result, conditional on it being truly positive. |
| False-negative Rate | Five years | Determine the odds of testing negative in a positive population. |
| Specificity | Five years | The probability of a negative test result conditional on a true negative. |
| False-positive Rate | Five years | Determine the odds of testing positive in a negative population. |
| Receiver Operating Characteristic Curve | Five years | The ROC curve is a curve based on a series of different dichotomous classifications (cut-off values or decision thresholds), with the rate of true positives (sensitivity) as the vertical coordinate and the rate of false positives (1-specificity) as the horizontal coordinate. |
| Area under roc Curve | Five years | AUC is defined as the area under the ROC curve enclosed with the axes, and the closer the AUC is to 1.0, the more authentic the assay is. |
Countries
China
Contacts
Primary ContactYan Nie, Prof.Dr.
Outcome results
None listed