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临床试验/NCT07129005
NCT07129005Enrolling By Invitation不适用

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

Tongji Hospital1 个研究点 分布在 1 个国家目标入组 520 人开始时间: 2025年1月1日最近更新:
适应症

试验速览

阶段
不适用
状态
Enrolling By Invitation
入组人数
520
试验地点
1
主要终点
Sensitivity

研究概览

简要总结

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.

详细描述

Uterine fibroids are the most common benign gynecological tumors among women of reproductive age in China, with an incidence that has been increasing annually. Statistics show that the prevalence of uterine fibroids among women over 30 years old in China has reached 20%-30%, and the onset age is trending younger. During the "12th Five-Year Plan" period, significant progress was made in the minimally invasive and pharmacological treatment of uterine fibroids through enhanced allocation of medical resources, advancement of clinical research, and improvement of diagnostic and treatment guidelines. However, with the rapid economic and social development in China, changes in environmental factors, lifestyle shifts, and delayed childbearing associated with improved living standards have contributed to a continued rise in the incidence of uterine fibroids. Uterine fibroids have now become a major public health issue affecting women's health in China.

Elucidating the mechanisms underlying the onset, recurrence, and malignant transformation of uterine fibroids, developing individualized treatment plans based on fertility preservation, and identifying high-risk populations to reduce disease progression and recurrence have become critical challenges in the field of reproductive health and women's and children's health research in China. Solving these issues is not only essential for improving women's health and well-being but also for enhancing population quality and reducing the healthcare burden.

In collaboration with the National Clinical Research Center for Obstetrics and Gynecology and regional medical centers (under construction), participating institutions will collect clinical, imaging, pathological, laboratory, and molecular testing data to establish a multicenter, systematic database. Machine learning algorithms will be used to develop early-warning models for malignant transformation and prognostic risk prediction models. Internal validation and optimization will be performed using different grouped datasets from this database, while large-scale data accumulated in Project 1 will be used for both internal and external validation, ultimately resulting in the construction of accurate and efficient early-warning and risk prediction models.

This multicenter retrospective observational study is led by Tongji Hospital in collaboration with several tertiary hospitals, including Zhongnan Hospital of Wuhan University, The Second Hospital of Shandong University, Shenzhen Second People's Hospital, West China Second University Hospital of Sichuan University, and The Third Affiliated Hospital of Zhengzhou University. The study protocol, including the use of existing inpatient and outpatient medical records, has been reviewed and approved by the Ethics Committee of Tongji Hospital (serving as the central IRB). Participating centers have either obtained approval from their local institutional review boards (IRBs) or formally accepted the central IRB approval. All procedures strictly adhere to the Declaration of Helsinki and relevant national ethical guidelines to ensure the protection of patient privacy and data confidentiality.

研究设计

研究类型
Observational
观察模型
Case Control
时间视角
Retrospective

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
Female
接受健康志愿者

入选标准

  • Histopathological confirmation of uterine sarcoma or leiomyoma.
  • Availability of preoperative MRI, includingT2WI and DWI, performed within 2 months of the surgery.

排除标准

  • 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.
  • 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.
  • 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.

结局指标

主要结局

Sensitivity

时间窗: through study completion, about July.2025

Ability of the test to correctly identify those with uterine sarcoma (true positive rate)

Specificity

时间窗: through study completion, about July.2025

Ability of the test to correctly identify those without uterine sarcoma (true negative rate)

Negative Predictive Value (NPV)

时间窗: through study completion, about July.2025

Probability that subjects with a negative test truly don't have the uterine fibroids

AUC

时间窗: through study completion, about July.2025

AUC stands for Area Under the Curve, specifically under the ROC (Receiver Operating Characteristic) curve

Positive Predictive Value (PPV)

时间窗: through study completion, about July.2025

Probability that subjects with a positive test truly have uterine sarcoma

次要结局

  • Intraclass Correlation Coefficient(Immediately after VOI delineation on baseline MRI)
  • SHapley Additive exPlanations(through study completion, about July.2025)
  • Comparative Performance of the Intratumoral, Peritumoral, and Combined Models(At model performance evaluation (following baseline imaging analysis),about August,2025)

研究者

申办方类型
Other
责任方
Principal Investigator
主要研究者

Wenwen Wang

associate professor

Tongji Hospital

研究点 (1)

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