Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours (STUMP)

January 13, 2026 updated by: Institut Bergonié
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 Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

392

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • France
      • Bordeaux, France
        • Recruiting
        • Institut Bergonie
        • Contact:
          • Sabrina CROCE

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

- Uterine smooth muscle tumors: leiomyomas, smooth muscle tumors of uncertain malignancy and leiomyossarcomas.

Description

Inclusion Criteria:

  • 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.

Exclusion Criteria:

  • na

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
Other: No intervention
No intervention since this is an observational study
Leiomyoma-leiomyosarcoma
Smooth muscle tumors of the uterus that do fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas)
Other: No intervention
No intervention since this is an observational study

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
throughout the conduct of the study - an expected average of 6 months after data collection

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Develop a prognostic tool for STUMP
Time Frame: 6 months after receiving the data.
Develop a model to predict progression-free survival for STUMP group based on the features extracted from Whole Slide Images.
6 months after receiving the data.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Institut Bergonié

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 1, 2023

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

December 1, 2026

Study Registration Dates

First Submitted

August 2, 2024

First Submitted That Met QC Criteria

August 2, 2024

First Posted (Actual)

August 6, 2024

Study Record Updates

Last Update Posted (Actual)

January 15, 2026

Last Update Submitted That Met QC Criteria

January 13, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IB2023-STUMP

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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