Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-24 @ 10:32 PM
Ignite Modification Date:
2025-12-24 @ 10:32 PM
NCT ID:
NCT07269535
Brief Summary:
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.
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:
NCT07269535
Study Brief:
Protocol Section:
NCT07269535