Radiomics-Based Non-Invasive MRI Differentiation of Uterine Sarcomas and Fibroids

NCT07129005 | Enrolling By Invitation DMC

• 1 other identifier: TJ-IRB20221167
• Study Type: observational
• Target: 520
• Locations: 1 country
• Sites: 1

Brief Summary

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

Uterine Fibroid; Uterine Sarcoma; Diagnose Disease; AI (Artificial Intelligence)

Keywords

Uterine sarcoma; Radiomics; Multicenter study; Uterine leiomyoma; Magnetic resonance imaging (MRI); Machine learning

Outcome Measures

Primary Outcomes (5)

• AUC

  AUC stands for Area Under the Curve, specifically under the ROC (Receiver Operating Characteristic) curve

  through study completion, about July.2025

• Sensitivity

  Ability of the test to correctly identify those with uterine sarcoma (true positive rate)

  through study completion, about July.2025

• Specificity

  Ability of the test to correctly identify those without uterine sarcoma (true negative rate）

  through study completion, about July.2025

• Positive Predictive Value (PPV)

  Probability that subjects with a positive test truly have uterine sarcoma

  through study completion, about July.2025

• Negative Predictive Value (NPV)

  Probability that subjects with a negative test truly don't have the uterine fibroids

  through study completion, about July.2025

Secondary Outcomes (3)

• Intraclass Correlation Coefficient

  Immediately after VOI delineation on baseline MRI

• SHapley Additive exPlanations

  through study completion, about July.2025

• Comparative Performance of the Intratumoral, Peritumoral, and Combined Models

  At model performance evaluation (following baseline imaging analysis),about August，2025

Study Arms (2)

Patients with a pathological diagnosis of uterine fibroids

Other: No intervention (observational study)

Patients with a pathological diagnosis of uterine sarcoma

Other: No intervention (observational study)

Interventions

No intervention (observational study) OTHER

No intervention (observational study)

Patients with a pathological diagnosis of uterine fibroids; Patients with a pathological diagnosis of uterine sarcoma

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will be enrolled in a multicenter study led by Tongji Hospital in collaboration with several tertiary hospitals. Patients with a pathological diagnosis of uterine leiomyoma or uterine sarcoma will be recruited for this trial.

You may qualify if:

• Histopathological confirmation of uterine sarcoma or leiomyoma.
• Availability of preoperative MRI, includingT2WI and DWI, performed within 2 months of the surgery.

You may not qualify if:

• 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.
• 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.
• 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.

Sponsors & Collaborators

• Tongji Hospital: lead

Study Sites (1)

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430000, China

Location

MeSH Terms

Conditions

Leiomyoma; Myofibroma

Interventions

Observation

Condition Hierarchy (Ancestors)

Neoplasms, Muscle Tissue; Neoplasms, Connective and Soft Tissue; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Connective Tissue; Connective Tissue Diseases; Skin and Connective Tissue Diseases

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: associate professor

Study Record Dates

• First Submitted: August 3, 2025
• First Posted: August 19, 2025
• Study Start: January 1, 2025
• Primary Completion: July 30, 2025
• Study Completion: December 30, 2025
• Last Updated: August 19, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)