No intervention (observational study): No intervention (observational study)
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.
Eligibility
Sex
FEMALE
Min age
18 Years
Max age
—
Healthy volunteers
No
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.
Primary outcome measure(s)
AUC — through study completion, about July.2025 AUC stands for Area Under the Curve, specifically under the ROC (Receiver Operating Characteristic) curve
Sensitivity — through study completion, about July.2025 Ability of the test to correctly identify those with uterine sarcoma (true positive rate)
Specificity — through study completion, about July.2025 Ability of the test to correctly identify those without uterine sarcoma (true negative rate）
Positive Predictive Value (PPV) — through study completion, about July.2025 Probability that subjects with a positive test truly have uterine sarcoma
Negative Predictive Value (NPV) — through study completion, about July.2025 Probability that subjects with a negative test truly don't have the uterine fibroids
Trial sites (1)
Facility
City
Region
Status
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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