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

NCT06540846 | Recruiting

• 1 other identifier: IB2023-STUMP
• Study Type: observational
• Target: 392
• Locations: 1 country
• Sites: 1

Brief 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). 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

Keywords

algorithm; diagnostic; pronostic

Outcome Measures

Primary Outcomes (1)

• 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.

  throughout the conduct of the study - an expected average of 6 months after data collection

Secondary Outcomes (1)

• Develop a prognostic tool for STUMP

  6 months after receiving the data.

Study Arms (2)

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

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

Interventions

No intervention OTHER

No intervention since this is an observational study

Leiomyoma-leiomyosarcoma; STUMP cohort

Eligibility Criteria

• Gender Eligibility Details: only uterin tumors
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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.

Sponsors & Collaborators

• Institut Bergonié: lead

Study Sites (1)

Institut Bergonie

Bordeaux, France

RECRUITING

Biospecimen

Retention: SAMPLES WITHOUT DNA

digitalized slides

MeSH Terms

Conditions

Disease

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Sabrina CROCE

CONTACT

+33556333333; s.croce@bordeaux.unicancer.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 2, 2024
• First Posted: August 6, 2024
• Study Start: December 1, 2023
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: January 15, 2026

Record last verified: 2026-01

Locations

FR (1)