protocolSection identificationModule nctId: NCT06286267, orgStudyIdInfo id: SYSKY-2023-351-02, briefTitle: AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors, statusModule overallStatus: RECRUITING, startDateStruct date: 2023-03-01, primaryCompletionDateStruct date: 2027-12-31, completionDateStruct date: 2027-12-31, studyFirstPostDateStruct date: 2024-02-29, lastUpdatePostDateStruct date: 2024-02-29, sponsorCollaboratorsModule leadSponsor name: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, class: OTHER, collaborators name: Sun Yat-sen University, collaborators name: Peking University Shenzhen Hospital, collaborators name: Guangdong Provincial Maternal and Child Health Hospital, collaborators name: The Third Affiliated Hospital of Guangzhou Medical University, descriptionModule briefSummary: 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., conditionsModule conditions: Phyllodes Breast Tumor, conditions: Artificial Intelligence, conditions: Multiomics, conditions: Prognostic Cancer Model, conditions: Diagnosis, designModule studyType: OBSERVATIONAL, designInfo observationalModel: COHORT, timePerspective: OTHER, enrollmentInfo count: 4000, type: ESTIMATED, armsInterventionsModule interventions name: imaging, outcomesModule primaryOutcomes measure: Sensitivity, primaryOutcomes measure: False-negative Rate, primaryOutcomes measure: Specificity, primaryOutcomes measure: False-positive Rate, primaryOutcomes measure: Receiver Operating Characteristic Curve, primaryOutcomes measure: Area under roc Curve, eligibilityModule sex: FEMALE, stdAges: CHILD, stdAges: ADULT, stdAges: OLDER_ADULT, contactsLocationsModule locations facility: Sun Yat-sen University Cancer Center, status: RECRUITING, city: Guangzhou, state: Guangdong, zip: 510050, country: China, contacts name: Feng Ye, Prof.Dr., role: CONTACT, phone: 15914388994, geoPoint lat: 23.11667, lon: 113.25, locations facility: Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, status: RECRUITING, city: Guangzhou, state: Guangdong, zip: 510120, country: China, contacts name: Yan Nie, Prof.Dr., role: CONTACT, phone: +86 020-81332587, email: nieyan7@mail.sysu.edu.cn, geoPoint lat: 23.11667, lon: 113.25, locations facility: The Third Affiliated Hospital of Guangzhou Medical University, status: RECRUITING, city: Guangzhou, state: Guangdong, zip: 510145, country: China, contacts name: Hui Mai, Prof.Dr., role: CONTACT, phone: 13925129112, geoPoint lat: 23.11667, lon: 113.25, locations facility: Guangdong Maternal and Child Health Hospital, status: RECRUITING, city: Guangzhou, state: Guangdong, zip: 511400, country: China, contacts name: Yu Tan, Prof.Dr., role: CONTACT, phone: 13632356526, geoPoint lat: 23.11667, lon: 113.25, hasResults: False