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

NCT06286267 | Recruiting

• 1 other identifier: SYSKY-2023-351-02
• Study Type: observational
• Target: 4,000
• Locations: 1 country
• Sites: 4

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.

Conditions

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

Outcome Measures

Primary Outcomes (6)

• Sensitivity

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

  Five years

• False-negative Rate

  Determine the odds of testing negative in a positive population.

  Five years

• Specificity

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

  Five years

• False-positive Rate

  Determine the odds of testing positive in a negative population.

  Five years

• Receiver Operating Characteristic Curve

  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.

  Five years

• Area under roc Curve

  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.

  Five years

Study Arms (1)

Breast phyllodes tumor

Patients diagnosed with phyllodes tumor of breast

Diagnostic Test: imaging

Interventions

imaging DIAGNOSTIC_TEST

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

Breast phyllodes tumor

Eligibility Criteria

• Sex: female
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients are all those who attended Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.

You may qualify if:

• Patients diagnosed with a phyllodes tumor of the breast

You may not qualify if:

• Blurred images, imaging artifacts

Sponsors & Collaborators

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

Study Sites (4)

Sun Yat-sen University Cancer Center

Guangzhou, Guangdong, 510050, China

RECRUITING

Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University

Guangzhou, Guangdong, 510120, China

RECRUITING

The Third Affiliated Hospital of Guangzhou Medical University

Guangzhou, Guangdong, 510145, China

RECRUITING

Guangdong Maternal and Child Health Hospital

Guangzhou, Guangdong, 511400, China

RECRUITING

Related Publications (10)

• Mishra SP, Tiwary SK, Mishra M, Khanna AK. Phyllodes tumor of breast: a review article. ISRN Surg. 2013;2013:361469. doi: 10.1155/2013/361469. Epub 2013 Mar 20.

  PMID: 23577269 BACKGROUND

• Belkacemi Y, Bousquet G, Marsiglia H, Ray-Coquard I, Magne N, Malard Y, Lacroix M, Gutierrez C, Senkus E, Christie D, Drumea K, Lagneau E, Kadish SP, Scandolaro L, Azria D, Ozsahin M. Phyllodes tumor of the breast. Int J Radiat Oncol Biol Phys. 2008 Feb 1;70(2):492-500. doi: 10.1016/j.ijrobp.2007.06.059. Epub 2007 Oct 10.

  PMID: 17931796 BACKGROUND

• Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019 Nov;16(11):703-715. doi: 10.1038/s41571-019-0252-y. Epub 2019 Aug 9.

  PMID: 31399699 BACKGROUND

• van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med. 2021 May;27(5):775-784. doi: 10.1038/s41591-021-01343-4. Epub 2021 May 14.

  PMID: 33990804 BACKGROUND

• Wang Y, Acs B, Robertson S, Liu B, Solorzano L, Wahlby C, Hartman J, Rantalainen M. Improved breast cancer histological grading using deep learning. Ann Oncol. 2022 Jan;33(1):89-98. doi: 10.1016/j.annonc.2021.09.007. Epub 2021 Sep 29.

  PMID: 34756513 BACKGROUND

• Chow ZL, Thike AA, Li HH, Nasir NDM, Yeong JPS, Tan PH. Counting Mitoses With Digital Pathology in Breast Phyllodes Tumors. Arch Pathol Lab Med. 2020 Nov 1;144(11):1397-1400. doi: 10.5858/arpa.2019-0435-OA.

  PMID: 32150458 BACKGROUND

• Cheng CL, Md Nasir ND, Ng GJZ, Chua KWJ, Li Y, Rodrigues J, Thike AA, Heng SY, Koh VCY, Lim JX, Hiew VJN, Shi R, Tan BY, Tay TKY, Ravi S, Ng KH, Oh KSL, Tan PH. Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor. Lab Invest. 2022 Mar;102(3):245-252. doi: 10.1038/s41374-021-00689-0. Epub 2021 Nov 24.

  PMID: 34819630 BACKGROUND

• Kates-Harbeck J, Svyatkovskiy A, Tang W. Predicting disruptive instabilities in controlled fusion plasmas through deep learning. Nature. 2019 Apr;568(7753):526-531. doi: 10.1038/s41586-019-1116-4. Epub 2019 Apr 17.

  PMID: 30996348 BACKGROUND

• Gong C, Nie Y, Qu S, Liao JY, Cui X, Yao H, Zeng Y, Su F, Song E, Liu Q. miR-21 induces myofibroblast differentiation and promotes the malignant progression of breast phyllodes tumors. Cancer Res. 2014 Aug 15;74(16):4341-52. doi: 10.1158/0008-5472.CAN-14-0125. Epub 2014 Jun 30.

  PMID: 24980553 RESULT

• Nie Y, Chen J, Huang D, Yao Y, Chen J, Ding L, Zeng J, Su S, Chao X, Su F, Yao H, Hu H, Song E. Tumor-Associated Macrophages Promote Malignant Progression of Breast Phyllodes Tumors by Inducing Myofibroblast Differentiation. Cancer Res. 2017 Jul 1;77(13):3605-3618. doi: 10.1158/0008-5472.CAN-16-2709. Epub 2017 May 16.

  PMID: 28512246 RESULT

MeSH Terms

Conditions

Phyllodes Tumor; Disease

Interventions

Diagnostic Imaging

Condition Hierarchy (Ancestors)

Sarcoma; Neoplasms, Connective and Soft Tissue; Neoplasms by Histologic Type; Neoplasms; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Diagnostic Techniques and Procedures; Diagnosis

Central Study Contacts

Yan Nie, Prof.Dr.

CONTACT

+86 020-81332587; nieyan7@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: February 22, 2024
• First Posted: February 29, 2024
• Study Start: March 1, 2023
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: February 29, 2024

Record last verified: 2024-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (4)