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

AI (Artificial Intelligence), Diagnose Disease, Uterine Fibroid, Uterine Sarcoma

Uterine sarcoma, Radiomics, Multicenter study, Uterine leiomyoma, Magnetic resonance imaging (MRI), Machine learning

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.

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.

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