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

NCT07129005 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 520

Last updated 2025-08-19

No results posted yet for this study

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)

Interventions

OTHER

No intervention (observational study)

No intervention (observational study)

Sponsors & Collaborators

  • Tongji Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-01
Primary Completion
2025-07-30
Completion
2025-12-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07129005 on ClinicalTrials.gov