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

StatusEnrolling by Invitation
PhaseNot specified
Started
View on ClinicalTrials.gov ↗

Record History

  1. Completion pushed from Dec 30, 2025 to Dec 30, 2029 Eligibility criteria expanded, about 5 words Show 1 more changeShow less
    • Eligibility criteria expanded, about 5 words
  2. Study First Submitted
  • Stopped: an ongoing trial was terminated, suspended or withdrawn.
  • Notable: the trial’s plan changed such as a big move in its timeline or size, a new primary endpoint, sponsor or phase, or recruiting opened or resumed.
  • Minor: routine progress and housekeeping, such as sites added or enrollment closed.
Tongji Hospital
200 trials monitored
See 3 trials at risk
Sarcoma
951 trials monitored
See 56 trials at risk
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.
Trial Details
NCT Number NCT07129005
Lead Sponsor Tongji Hospital
Conditions Uterine Fibroid, Uterine Sarcoma, Diagnose Disease, AI (Artificial Intelligence)
Enrollment 520 participants
Start Date
Primary Completion (estimated)
Study Completion (estimated)
Updated on ClinicalTrials.gov