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

Development of an Artificial Intelligence-Based System for Precise 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.

Study Overview

• Status: Recruiting
• Conditions: Artificial Intelligence; Diagnosis; Phyllodes Breast Tumor; Multiomics; Prognostic Cancer Model
• Intervention / Treatment: Diagnostic test: imaging

• Study Type: Observational
• Enrollment (Estimated): 4000

Contacts and Locations

• Study Contact: Name: Yan Nie, Prof.Dr.; Phone Number: +86 020-81332587; Email: nieyan7@mail.sysu.edu.cn

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510120
      • Recruiting
      • Sun Yat-sen Memorial Hospital, Sun Yat-sen University
      • Contact: Yan Nie, Prof.Dr.; Phone Number: +86 020-81332587; Email: nieyan7@mail.sysu.edu.cn
    • Guangzhou, Guangdong, China, 510050
      • Recruiting
      • Sun Yat-Sen University Cancer Center
      • Contact: Feng Ye, Prof.Dr.; Phone Number: 15914388994
    • Guangzhou, Guangdong, China, 510145
      • Recruiting
      • The Third Affiliated Hospital of Guangzhou Medical University
      • Contact: Hui Mai, Prof.Dr.; Phone Number: 13925129112
    • Guangzhou, Guangdong, China, 511400
      • Recruiting
      • Guangdong Maternal and Child Health Hospital
      • Contact: Yu Tan, Prof.Dr.; Phone Number: 13632356526

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

• Patients diagnosed with a phyllodes tumor of the breast

Exclusion Criteria:

• Blurred images, imaging artifacts

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Breast phyllodes tumor; Patients diagnosed with phyllodes tumor of breast	Diagnostic test: imaging; Patient medical imaging materials including ultrasound, mammography, CT, MRI

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

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

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2023
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: February 22, 2024
• First Submitted That Met QC Criteria: February 22, 2024
• First Posted (Estimated): February 29, 2024

Study Record Updates

• Last Update Posted (Estimated): February 29, 2024
• Last Update Submitted That Met QC Criteria: February 22, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Skin Diseases; Neoplasms, Connective and Soft Tissue; Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Breast Diseases; Sarcoma; Breast Neoplasms; Disease; Phyllodes Tumor
• Other Study ID Numbers: SYSKY-2023-351-02

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: no plan to make individual participant data available to other researchers.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No