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

Recruiting

• Conditions: Stump
• Interventions: Other: No intervention

• Registration Number: NCT06540846
• Lead Sponsor: Institut Bergonié

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-12-01
• Status Verified: 2024-08
• Study First Submitted: 2024-08-02
• Study First Submitted QC: 2024-08-02
• Last Update Submitted: 2024-08-02
• Study First Posted: 2024-08-06
• Last Update Posted: 2024-08-06
• Primary Completion: 2024-12
• Study Completion: 2024-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: Female
• Target Recruitment: 392

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).

Exclusion Criteria

• na

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
STUMP cohort	No intervention	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
Leiomyoma-leiomyosarcoma	No intervention	Smooth muscle tumors of the uterus that do fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas)

Primary Outcome Measures

Name	Time	Method
Develop deep learning models that can accurately subclassify gynaecologic smooth muscle tumours	throughout the conduct of the study - an expected average of 6 months after data collection	Develop deep learning based on digitalized slides

Trial Locations

Locations (1)

Institut Bergonie; 🇫🇷 Bordeaux, France