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An MRI-Based Study of Intelligent Pathological Subtyping and Grading of Renal Tumors

NCT07743749 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 900

Last updated 2026-08-04

No results posted yet for this study

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.

Conditions

  • Renal Tumor
  • Kidney Neoplasm

Interventions

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

  • Peking University People's Hospital

    collaborator OTHER

  • Shanxi Province Cancer Hospital

    collaborator OTHER

  • Chinese PLA General Hospital

    collaborator OTHER

  • RenJi Hospital

    collaborator OTHER

  • Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center

    collaborator OTHER

  • Cancer Institute and Hospital, Chinese Academy of Medical Sciences

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-01-01
Primary Completion
2026-12-31
Completion
2026-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07743749 on ClinicalTrials.gov