Clinical trial · Observational
Radiomics-Based Non-Invasive MRI Differentiation of Uterine Sarcomas and Fibroids
Non-invasive Differentiation of Uterine Sarcomas From Uterine Fibroids Using Multiparametric MRI Radiomics
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This retrospective case-control study aims to develop and validate a diagnostic model based on multimodal big data and artificial intelligence to differentiate uterine leiomyoma from uterine sarcoma. Investigators will extract historical case data from existing inpatient and outpatient records, including medical history, physical and gynecological examination findings, MRI imaging data, laboratory results, and pathological records. The study seeks to address the question of whether integrating diverse retrospective clinical data with advanced AI techniques can accurately classify uterine tumors as benign leiomyomas or malignant sarcomas, thereby supporting clinical decision-making and optimizing diagnostic workflows.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| AI (Artificial Intelligence) | — | UNRESOLVED | — |
| Diagnose Disease | — | UNRESOLVED | — |
| Uterine Fibroid | Uterine Corpus Leiomyoma | ALIAS | 0.90 |
| Uterine Sarcoma | Uterine Corpus Sarcoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| No intervention (observational study) | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Patients with a pathological diagnosis of uterine fibroids
- interventionNames
- Other: No intervention (observational study)
- label
- Patients with a pathological diagnosis of uterine sarcoma
- interventionNames
- Other: No intervention (observational study)
Primary outcomes (5)
- measure
- AUC
- timeFrame
- through study completion, about July.2025
- description
- AUC stands for Area Under the Curve, specifically under the ROC (Receiver Operating Characteristic) curve
- measure
- Sensitivity
- timeFrame
- through study completion, about July.2025
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. Histopathological confirmation of uterine sarcoma or leiomyoma. 2. Availability of preoperative MRI, includingT2WI and DWI, performed within 2 months of the surgery. Exclusion Criteria: 1. Tumors smaller than 2 cm. Small tumors may be difficult to accurately perform segmentation and feature extraction, which may affect the accuracy and reliability of the model. 2. Non-primary uterine sarcomas. Sarcomas from other sites with metastasis to the uterus were excluded because the biological characteristics and imaging findings of these tumors may differ from those of primary uterine sarcomas and may lead to bias in the diagnostic model. 3. Concurrent pelvic malignancies. To avoid the influence of other types of tumors on the imaging features of uterine sarcoma and leiomyoma, and to ensure the pertinence and accuracy of the model.
References
Publications (0)
Data not yet available