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An MRI-Based Study of Intelligent Pathological Subtyping and Grading of Renal Tumors
Sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Summary
This retrospective + prospective, non-interventional study aims to develop and evaluate artificial intelligence methods for the detection, pathological subtyping, and histological grading of renal tumors using magnetic resonance imaging (MRI). Approximately 900 adult patients with available preoperative renal MRI examinations and postoperative pathological results will be included. The pathological findings will be used as the reference standard for model development and evaluation. In addition to MRI data, selected demographic, clinical, and laboratory information may be incorporated to improve model performance. The study will not change participants' diagnosis, treatment, or follow-up, and no additional examinations or interventions will be required. All study data will be de-identified before analysis. The ultimate goal is to develop an MRI-based intelligent diagnostic approach that may assist clinicians in the preoperative assessment and individualized management of patients with renal tumors.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
900
Start Date
2021-01-01
Completion Date
2026-12-31
Last Updated
2026-08-04
Healthy Volunteers
No
Conditions
Interventions
MRI-Based Artificial Intelligence Analysis
Existing preoperative multisequence renal MRI images, including T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, fat-suppressed imaging, and contrast-enhanced imaging when available, were retrospectively analyzed using artificial intelligence and deep learning methods. The models were developed to detect and segment renal tumors and to predict pathological subtype and histological grade. Postoperative pathological findings were used as the reference standard. No additional MRI examination or diagnostic procedure was performed for the study.
Locations (1)
Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China