Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours (STUMP)
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Sabrina CROCE
- Phone Number: +33556333333
- Email: s.croce@bordeaux.unicancer.fr
Study Locations
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-
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Bordeaux, France
- Recruiting
- Institut Bergonie
-
Contact:
- Sabrina CROCE
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
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
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / 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
|
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)
|
No intervention since this is an observational study
|
What is the study measuring?
Primary Outcome Measures
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
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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.
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throughout the conduct of the study - an expected average of 6 months after data collection
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Develop a prognostic tool for STUMP
Time Frame: 6 months after receiving the data.
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Develop a model to predict progression-free survival for STUMP group based on the features extracted from Whole Slide Images.
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6 months after receiving the data.
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Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- IB2023-STUMP
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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