Stump
Conditions
Keywords
algorithm, diagnostic, pronostic
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.
Interventions
OTHERNo intervention
No intervention since this is an observational study
Sponsors
Institut Bergonié
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Develop a prognostic tool for STUMP | 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. |
Countries
France
Contacts
Primary ContactSabrina CROCE
Outcome results
None listed