A Prospective Validation Study of Radiomics in the Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma

In our previous study, based on the multi-center clinical big data collected from January 2012 to January 2025, we have completed the construction of a multimodal early warning model for the malignant transformation of uterine fibroids. The model was mainly based on T2WI and DWI sequences, and was trained and optimized by support vector machine (SVM) algorithm. In the retrospective study and internal validation, the model shows high sensitivity and specificity, which preliminarily proves that it has good application potential in identifying high-risk groups and predicting the risk of malignant transformation of uterine fibroids.

However, there are still some limitations in retrospective studies and internal validation results, and its application value, universality and stability in real clinical environment have not been fully verified. Therefore, we plan to conduct a prospective validation study in consecutive patients enrolled after January 2025 to evaluate the clinical performance and generalization of the model in predicting the malignant tendency or risk of malignant transformation of uterine fibroids through practical application in the real population, and further analyze the operability in the actual diagnosis and treatment process and the potential value for patient management. This study will provide reliable evidence for early screening, follow-up management and individualized treatment of high-risk population, and has important clinical and public health significance for improving the early diagnosis rate, reducing the risk of malignant transformation and improving the prognosis of patients with uterine fibroids.

Study Overview

• Status: Not yet recruiting
• Conditions: MRI; Uterine Sarcoma; Uterine Fibroid; AI (Artificial Intelligence); Radiomic; Prospective Observational Study
• Intervention / Treatment: Other: No intervention (observational study)

Detailed Description

Uterine fibroids are the most common benign gynecological tumors among women of reproductive age in China, with a prevalence of 20-30% among women over 30 years old and a trend toward younger onset. Despite advances in minimally invasive techniques and pharmacological therapies during the "12th Five-Year Plan," the incidence of uterine fibroids continues to rise due to rapid socioeconomic development, environmental changes, lifestyle shifts, and delayed childbearing. As a result, uterine fibroids have become a major public health concern. Understanding the mechanisms underlying the onset, recurrence, and malignant transformation of fibroids, developing fertility-preserving individualized treatment strategies, and identifying high-risk populations remain key challenges in reproductive and women's health research.

To address these challenges, our multicenter collaborative group, led by Tongji Hospital and supported by the National Clinical Research Center for Obstetrics and Gynecology, has established a large-scale systematic database integrating clinical, imaging, pathological, laboratory, and molecular data from multiple tertiary hospitals. Based on multicenter clinical big data collected from January 2012 to January 2025, we have developed a multimodal early-warning model for the malignant transformation of uterine fibroids. This model, primarily incorporating T2WI and DWI features and optimized using a support vector machine (SVM) algorithm, demonstrated high sensitivity and specificity in retrospective analysis and internal validation, suggesting promising potential for identifying high-risk individuals.

However, retrospective designs inherently limit the assessment of the model's real-world clinical applicability, generalizability, and stability. Therefore, beginning in January 2025, we plan to conduct a prospective validation study in consecutively enrolled patients to evaluate the model's diagnostic performance in routine clinical practice, its feasibility in real-world diagnostic workflows, and its potential value for early screening, follow-up management, and individualized treatment of high-risk populations. This study is expected to provide robust evidence to improve early detection, reduce malignant transformation risk, and ultimately enhance clinical outcomes and public health impact.

• Study Type: Observational
• Enrollment (Estimated): 500

Contacts and Locations

• Study Contact: Name: WENWEN WANG; Phone Number: 15927167698; Email: wenwenwang@hust.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients with a pathological diagnosis of uterine leiomyoma or uterine sarcoma will be recruited.

Description

1. Inclusion Criteria:

   1.1 Patients clinically evaluated and radiologically examined (including MRI, particularly T2WI and DWI sequences) who are diagnosed with uterine leiomyoma or considered highly suspected of uterine sarcoma, in combination with preliminary pathological findings.

   1.2 Patients scheduled for surgical treatment or those eligible for long-term standardized follow-up.

   1.3 Patients who are able to understand the study procedures and voluntarily sign the written informed consent form.

2. Exclusion Criteria:

2.1 Patients with severe organic diseases or a previous confirmed diagnosis of other malignant uterine tumors.

2.2 Patients unable to complete baseline examinations, unable to comply with long-term follow-up, or unwilling to provide written informed consent.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Patients with a pathological diagnosis of uterine fibroids	Other: No intervention (observational study); This study is a retrospective observational study without intervention.
Patients with a pathological diagnosis of uterine fibroids or uterine sarcoma	Other: No intervention (observational study); This study is a retrospective observational study without intervention.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AUC	AUC stands for Area Under the Curve, specifically under the ROC (Receiver Operating Characteristic) curve	Histopathological diagnosis obtained from surgical specimens within 1 week after imaging examinations.
Sensitivity	Ability of the test to correctly identify those with uterine sarcoma (true positive rate)	Histopathological diagnosis obtained from surgical specimens within 1 week after imaging examinations.
Specificity	Ability of the test to correctly identify those without uterine sarcoma (true negative rate）	Histopathological diagnosis obtained from surgical specimens within 1 week after imaging examinations.

Collaborators and Investigators

• Sponsor: Tongji Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): November 30, 2025
• Primary Completion (Estimated): January 1, 2050
• Study Completion (Estimated): January 1, 2050

Study Registration Dates

• First Submitted: November 26, 2025
• First Submitted That Met QC Criteria: November 26, 2025
• First Posted (Actual): December 8, 2025

Study Record Updates

• Last Update Posted (Actual): December 8, 2025
• Last Update Submitted That Met QC Criteria: November 26, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: MRI; Uterine Fibroid; uterine sarcoma; prospective observational study; Radiomic; AI (Artificial Intelligence)
• Additional Relevant MeSH Terms: Neoplasms; Neoplasms by Histologic Type; Neoplasms, Connective and Soft Tissue; Neoplasms, Muscle Tissue; Leiomyoma; Investigative Techniques; Methods; Observation
• Other Study ID Numbers: TJ-IRB202511040

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No