Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: No intervention.
- Who it may be relevant to
- Registry conditions: Stump. Basic parameters: No limits · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- France
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Interventions
- Other No intervention
No intervention since this is an observational study
Primary outcome measures
- 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]
Secondary outcome measures (1)
- Develop a prognostic tool for STUMP [Time frame: 6 months after receiving the data.]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
France · 1 center
- Institut Bergonie — Bordeaux
Identifiers
NCT: NCT06540846 · IB2023-STUMP