Phyllodes Breast TumorNCT06286267
AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors
An observational study of Phyllodes Breast Tumor and Artificial Intelligence, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 4 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Phyllodes Breast Tumor, Artificial Intelligence, Multiomics
- What is being tested?
- imaging
- How many people
- 4,000 participants
- Where
- 4 sitesChina
- Study dates
- 1 Mar 2023to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- Women only
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients are all those who attended Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
Who may be able to join
- Patients diagnosed with a phyllodes tumor of the breast
Who cannot join
- Blurred images, imaging artifacts
Trial locations and contacts
4 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China4 sites, 4 recruiting
- Recruiting
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, 510050
Feng Ye, Prof.Dr., 15914388994
- Recruiting
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
Guangzhou, Guangdong, 510120
Yan Nie, Prof.Dr., +86 020-81332587, nieyan7@mail.sysu.edu.cn
- Recruiting
The Third Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510145
Hui Mai, Prof.Dr., 13925129112
- Recruiting
Guangdong Maternal and Child Health Hospital
Guangzhou, Guangdong, 511400
Yu Tan, Prof.Dr., 13632356526
About this study
Official title: 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.
Read more
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
- Being tested
- imaging
- Medical subject headings
- Phyllodes Tumor, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
imagingDiagnostic test
Patient medical imaging materials including ultrasound, mammography, CT, MRI
| Study group | Role | What they receive |
|---|---|---|
| Breast phyllodes tumor | Patients diagnosed with phyllodes tumor of breast Receives: Diagnostic Test: imaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
The probability of a positive test result, conditional on it being truly positive.
Measured: Five years
False-negative Rate
Determine the odds of testing negative in a positive population.
Measured: Five years
Specificity
The probability of a negative test result conditional on a true negative.
Measured: Five years
False-positive Rate
Determine the odds of testing positive in a negative population.
Measured: 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.
Measured: 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.
Measured: Five years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Feb 2024
- Study start1 Mar 2023
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Phyllodes Breast TumorStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Sun Yat-sen University, Peking University Shenzhen Hospital, Guangdong Provincial Maternal and Child Health Hospital, The Third Affiliated Hospital of Guangzhou Medical University
- Responsible party
- Principal investigator, nieyan, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06286267
- Sponsor's protocol ID
- SYSKY-2023-351-02
- Status last verified
- Feb 2024
- First submitted
- 22 Feb 2024
- Last updated
- 29 Feb 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: no plan to make individual participant data available to other researchers.
Publications
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.
PubMed 23577269Belkacemi 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.
PubMed 17931796Bera 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.
PubMed 31399699van 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.
PubMed 33990804Wang 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.
PubMed 34756513Chow 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.
PubMed 32150458Cheng 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.
PubMed 34819630Kates-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.
PubMed 30996348Gong 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.
PubMed 24980553Nie 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.
PubMed 28512246
Study at a glance
From the registry record, last updated by the sponsor on 29 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an Artificial Intelligence-Based System for Precise Diagnosis and Prognosis of Breast Phyllodes Tumors
- ClinicalTrials.gov ID
- NCT06286267
- Acronym / sponsor ID
- SYSKY-2023-351-02
- Conditions
- Phyllodes Breast Tumor, Artificial Intelligence, Multiomics, Prognostic Cancer Model, Diagnosis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- Sun Yat-sen University, Peking University Shenzhen Hospital, Guangdong Provincial Maternal and Child Health Hospital, The Third Affiliated Hospital of Guangzhou Medical University
- Study start
- 1 Mar 2023 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 29 Feb 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Breast phyllodes tumor | – | Diagnostic Test: imagingPatients diagnosed with phyllodes tumor of breast |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients are all those who attended Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
Key inclusion criteria (1 listed)
- Patients diagnosed with a phyllodes tumor of the breast
Key exclusion criteria (1 listed)
- Blurred images, imaging artifacts
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| imagingPatient medical imaging materials including ultrasound, mammography, CT, MRI | Diagnostic test | Breast phyllodes tumor |
Primary outcome
Sensitivity
Time frame: Five years
The probability of a positive test result, conditional on it being truly positive.
False-negative Rate
Time frame: Five years
Determine the odds of testing negative in a positive population.
Specificity
Time frame: Five years
The probability of a negative test result conditional on a true negative.
False-positive Rate
Time frame: Five years
Determine the odds of testing positive in a negative population.
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Feb 2024.
- Background: PubMed 23577269Mishra 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.
- Background: PubMed 17931796Belkacemi 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.
- Background: PubMed 31399699Bera 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.
- Background: PubMed 33990804van 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.
- Background: PubMed 34756513Wang 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.
- Background: PubMed 32150458Chow 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.
- Background: PubMed 34819630Cheng 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.
- Background: PubMed 30996348Kates-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.
- Results publication: PubMed 24980553Gong 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.
- Results publication: PubMed 28512246Nie 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.
- Individual participant dataSharing planned: No. no plan to make individual participant data available to other researchers.
Related trials for Phyllodes Breast Tumor
Not yet recruiting
Molecular Subtype-Guided Postoperative Radiotherapy for Phyllodes Tumor of the Breast
Radiotherapy, Adjuvant, Phyllodes Breast Tumor
Not yet recruitingObservational study
Dynamic ctDNA Monitoring in Advanced Malignant Phyllodes Tumors of the Breast
Malignant Phyllodes Tumor of the Breast
Active, not recruitingPhase 1
C7R-GD2.CART Cells for Patients With Relapsed or Refractory Neuroblastoma and Other GD2 Positive Cancers (GAIL-N)
Relapsed Neuroblastoma, Refractory Neuroblastoma
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Phyllodes Breast Tumor and Artificial Intelligence, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 4 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 29 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yan Nie, Prof.Dr. (+86 020-81332587, nieyan7@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: Five years). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06286267, which also lists 10 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Feb 2024.