StumpSTUMPNCT06540846
Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours
An observational study of Stump, run by Institut Bergonié at 1 site in France.
Trial summary
Smooth muscle tumors of the uterus that do not fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) are called STUMP (smooth muscle tumor of uncertain malignant potential). Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Stump
- How many people
- 392 participants
- Where
- 1 siteFrance
- Study dates
- 1 Dec 2023to Dec 2026
- 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: - Uterine smooth muscle tumors: leiomyomas, smooth muscle tumors of uncertain malignancy and leiomyossarcomas.
Who may be able to join
- Patients with a diagnosis of uterine smooth muscle tumors (leiomyomas, smooth muscle tumors of uncertain malignancy and leiomyosarcomas), registered in the RRePS database and/or treated at Institut Bergonié or one of the participating centers.
- Histopathological material available (kerosene blocks and/or slides).
- The follow-up (outcome) is required for each LMS/ STUMP.
Who cannot join
- na
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Smooth muscle tumors of the uterus that do not fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) are called STUMP (smooth muscle tumor of uncertain malignant potential). A potential solution to this problem could be the application of predictive models using artificial intelligence (AI) to aid in the histopathological classification and prognosis of gynecological smooth muscle tumors. Deep learning using convolutional neural networks represents a specific class of machine learning, in which predictive models are trained by considering small groups of pixels in digital images and iteratively identifying salient features. In this study, we aim to develop deep learning models capable of accurately subclassifying and predicting the prognosis of gynecological smooth muscle tumors, based on histopathological features of hematoxylin and eosin (H\&E) slides. The aim is to develop a diagnostic and prognostic algorithm to help pathologists better classify and diagnose uterine smooth muscle tumors and predict their clinical course.
- Conditions
- Stump
- Being tested
- No intervention
- Keywords
- algorithm, diagnostic, pronostic
- Medical subject headings
- Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
No intervention since this is an observational study
| Study group | Role | What they receive |
|---|---|---|
| STUMP cohort | Smooth muscle tumors of the uterus that do not fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) : smooth muscle tumor of uncertain malignant potential Receives: Other: No intervention | |
| Leiomyoma-leiomyosarcoma | Smooth muscle tumors of the uterus that do fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Develop deep learning models that can accurately subclassify gynaecologic smooth muscle tumours
This project aims to improve the diagnosis and prognosis of gynecologic smooth muscle tumors, including leiomyomas (LM), leiomyosarcomas (LMS), and smooth muscle tumors of uncertain malignant potential (STUMP). In detail, a workflow comprising 2 stages will be developed to automatically classify GSMT subtypes from whole-slide images and to predict progression-free survival for patients in the LMS and STUMP groups, thereby providing clinicians with a more effective tool to improve workflow quality.
Measured: throughout the conduct of the study - an expected average of 6 months after data collection
Secondary outcomes (1)
Develop a prognostic tool for STUMP
Develop a model to predict progression-free survival for STUMP group based on the features extracted from Whole Slide Images.
Measured: 6 months after receiving the data.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Aug 2024
- Study start1 Dec 2023
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for StumpStudies of the same condition, open and under way.
- 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
- Retrospective
- Enrolment
- 392 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Institut Bergonié
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06540846
- Sponsor's protocol ID
- IB2023-STUMP
- Acronym
- STUMP
- Status last verified
- Jan 2026
- First submitted
- 2 Aug 2024
- Last updated
- 15 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours
- ClinicalTrials.gov ID
- NCT06540846
- Acronym / sponsor ID
- STUMP / IB2023-STUMP
- Conditions
- Stump
- Keywords
- algorithm, diagnostic, pronostic
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 392 participants (estimated)
- Lead sponsor
- Institut BergoniéUniversity, hospital or other
- Study start
- 1 Dec 2023 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 6 Aug 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.
Smooth muscle tumors of the uterus that do not fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) are called STUMP (smooth muscle tumor of uncertain malignant potential). A potential solution to this problem could be the application of predictive models using artificial intelligence (AI) to aid in the histopathological classification and prognosis of gynecological smooth muscle tumors. Deep learning using convolutional neural networks represents a specific class of machine learning, in which predictive models are trained by considering small groups of pixels in digital images and iteratively identifying salient features. In this study, we aim to develop deep learning models capable of accurately subclassifying and predicting the prognosis of gynecological smooth muscle tumors, based on histopathological features of hematoxylin and eosin (H\&E) slides. The aim is to develop a diagnostic and prognostic algorithm to help pathologists better classify and diagnose uterine smooth muscle tumors and predict their clinical course.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| STUMP cohort | – | Other: No interventionSmooth muscle tumors of the uterus that do not fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) : smooth muscle tumor of uncertain malignant potential |
| Leiomyoma-leiomyosarcoma | – | Other: No interventionSmooth muscle tumors of the uterus that do fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- - Uterine smooth muscle tumors: leiomyomas, smooth muscle tumors of uncertain malignancy and leiomyossarcomas.
Key inclusion criteria (3 listed)
- Patients with a diagnosis of uterine smooth muscle tumors (leiomyomas, smooth muscle tumors of uncertain malignancy and leiomyosarcomas), registered in the RRePS database and/or treated at Institut Bergonié or one of the participating centers.
- Histopathological material available (kerosene blocks and/or slides).
- The follow-up (outcome) is required for each LMS/ STUMP.
Key exclusion criteria (1 listed)
- na
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 |
|---|---|---|
| No interventionNo intervention since this is an observational study | Other | Leiomyoma-leiomyosarcoma, STUMP cohort |
Primary outcome
Develop deep learning models that can accurately subclassify gynaecologic smooth muscle tumours
Time frame: throughout the conduct of the study - an expected average of 6 months after data collection
This project aims to improve the diagnosis and prognosis of gynecologic smooth muscle tumors, including leiomyomas (LM), leiomyosarcomas (LMS), and smooth muscle tumors of uncertain malignant potential (STUMP). In detail, a workflow comprising 2 stages will be developed to automatically classify GSMT subtypes from whole-slide images and to predict progression-free survival for patients in the LMS and STUMP groups, thereby providing clinicians with a more effective tool to improve workflow quality.
1 secondary outcome
Develop a prognostic tool for STUMP
Time frame: 6 months after receiving the data.
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 15 Jan 2026.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stump, run by Institut Bergonié at 1 site in France.
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 3 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 15 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. 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 Sabrina CROCE (+33556333333, s.croce@bordeaux.unicancer.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Develop deep learning models that can accurately subclassify gynaecologic smooth muscle tumours (time frame: throughout the conduct of the study - an expected average of 6 months after data collection). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 15 Jan 2026.