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NCT Number: NCT06286267

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

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.

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Key information

Conditions

Phyllodes Breast Tumor Artificial Intelligence Diagnosis Disease Multiomics Neoplasms Neoplasms by Histologic Type Neoplasms, Connective and Soft Tissue Pathologic Processes Pathological Conditions, Signs and Symptoms Phyllodes Tumor Prognostic Cancer Model Sarcoma

Sex eligibility

Female

Study type

Observational

Primary location

Guangdong Maternal and Child Health Hospital, Guangzhou, Guangdong, China

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Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Patients diagnosed with a phyllodes tumor of the breast

Exclusion criteria

  • Blurred images, imaging artifacts

Treatment and study plan

Imaging

Diagnostic Test

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

Primary outcomes

  1. Sensitivity

    Time frame: Five years

    The probability of a positive test result, conditional on it being truly positive.

  2. False-negative Rate

    Time frame: Five years

    Determine the odds of testing negative in a positive population.

  3. Specificity

    Time frame: Five years

    The probability of a negative test result conditional on a true negative.

  4. False-positive Rate

    Time frame: Five years

    Determine the odds of testing positive in a negative population.

  5. Receiver Operating Characteristic Curve

    Time frame: 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.

  6. Area under roc Curve

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Yan Nie, Prof.Dr.

CONTACT

[email protected]

+86 020-81332587

Sponsors and collaborators

Lead sponsor

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

Other

Collaborators

  • Guangdong Provincial Maternal and Child Health Hospital
  • Peking University Shenzhen Hospital
  • Sun Yat-sen University
  • The Third Affiliated Hospital of Guangzhou Medical University

Registry information

Official study title

Development of an Artificial Intelligence-Based System for Precise Diagnosis and Prognosis of Breast Phyllodes Tumors

Important dates

Study start
2023
Primary completion
2027
Study completion
2027
First posted
Feb 29, 2024
Registry last updated
Feb 29, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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