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

NCT07743749 | Recruiting DMC

• 1 other identifier: NCC-020738
• Study Type: observational
• Target: 900
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Magnetic Resonance Imaging; Artificial Intelligence; Deep Learning; Renal Tumor; Pathological Subtyping; Histological Grading; Multimodal Learning; Computer-Aided Diagnosis; Tumor Segmentation; Rare Renal Tumor Subtypes

Outcome Measures

Primary Outcomes (2)

• Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors

  The pathological subtype predicted by the MRI-based artificial intelligence model will be compared with the postoperative pathological diagnosis as the reference standard in the held-out test dataset. Accuracy will be calculated as the number of correctly classified renal tumors divided by the total number of renal tumors evaluated. Classification performance for individual pathological subtypes will also be summarized using sensitivity, specificity, and F1 score, where applicable.

  At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.

• Accuracy of MRI-Based Artificial Intelligence for Histological Grading of Malignant Renal Tumors

  The histological grade predicted by the MRI-based artificial intelligence model will be compared with the postoperative pathological grade as the reference standard. Histological grading will be assessed according to the four-tier World Health Organization/International Society of Urological Pathology grading system. Accuracy will be calculated as the number of malignant renal tumors with correctly predicted histological grade divided by the total number of malignant renal tumors evaluated.

  At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.

Secondary Outcomes (2)

• Performance of the Artificial Intelligence Model for Renal Tumor Detection

  At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.

• Accuracy of Artificial Intelligence-Based Renal Tumor Segmentation

  At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.

Study Arms (1)

Patients With Renal Tumors

Adult patients with pathologically confirmed renal tumors who underwent preoperative multisequence renal MRI as part of routine clinical care and had available pathological subtype and, when applicable, histological grade information. Existing de-identified MRI, pathological, clinical, and laboratory data were collected for artificial intelligence model development and evaluation. No additional examination, treatment, or study-specific intervention was administered.

Diagnostic Test: MRI-Based Artificial Intelligence Analysis

Interventions

MRI-Based Artificial Intelligence Analysis DIAGNOSTIC_TEST

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.

Patients With Renal Tumors

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This retrospective + prospective, hospital-based study includes adult patients with renal tumors who underwent preoperative renal MRI as part of routine clinical care and had corresponding pathological diagnoses. Eligible cases include benign and malignant renal tumor subtypes. Existing de-identified MRI, pathological, demographic, clinical, and laboratory data are collected for artificial intelligence model development and evaluation. No healthy volunteers are included, and no additional examinations, treatments, or study-specific follow-up are performed.

You may qualify if:

• Patients aged 18 years or older.
• Patients diagnosed with a renal tumor.
• Availability of preoperative renal magnetic resonance imaging examinations.
• Availability of a corresponding pathological diagnosis, including pathological subtype and, where applicable, histological grade.
• Magnetic resonance images that can be successfully retrieved and are of - - sufficient quality for image analysis.

You may not qualify if:

• Absence of renal magnetic resonance imaging data.
• Absence of a corresponding pathological diagnosis or insufficient pathological subtype or grading information.
• Magnetic resonance images that cannot be retrieved, opened, or read.
• Poor image quality that precludes reliable image annotation or artificial intelligence analysis.

Sponsors & Collaborators

• Cancer Institute and Hospital, Chinese Academy of Medical Sciences: lead
• Peking University People's Hospital: collaborator
• Shanxi Province Cancer Hospital: collaborator
• Chinese PLA General Hospital: collaborator
• RenJi Hospital: collaborator
• Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center: collaborator

Study Sites (1)

Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College

Beijing, China

RECRUITING

MeSH Terms

Conditions

Carcinoma, Renal Cell; Kidney Neoplasms

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases

Central Study Contacts

Xiongjun Ye

CONTACT

010-87787170; yexiongjun@cicams.ac.cn

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Physician

Study Record Dates

• First Submitted: July 29, 2026
• First Posted: August 4, 2026
• Study Start: January 1, 2021
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: August 4, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)