Clinical Research Directory

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

Sponsor: Tongji Hospital

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.

Official title: Non-invasive Differentiation of Uterine Sarcomas From Uterine Fibroids Using Multiparametric MRI Radiomics

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