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

29 juli 2026 bijgewerkt door: Xiongjun Ye, Cancer Institute and Hospital, Chinese Academy of Medical Sciences
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.

Studie Overzicht

Toestand

Werving

Conditie

Interventie / Behandeling

Gedetailleerde beschrijving

Renal tumors include multiple benign and malignant pathological subtypes with substantial differences in biological behavior, treatment strategy, and prognosis. Surgical planning and clinical management are closely related to the pathological subtype and histological grade of the tumor. However, accurately determining these pathological characteristics before surgery using conventional MRI interpretation remains challenging.

This is a retrospective + prospective, observational, and non-interventional study. Adult patients with renal tumors will be identified from existing clinical records. Eligible patients will have available renal MRI examinations and corresponding pathological diagnoses, including pathological subtype and, when applicable, histological grade. Cases with unreadable MRI data or images of insufficient quality for analysis will be excluded.

Existing study data will include multisequence MRI examinations, such as T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, fat-suppressed imaging, and contrast-enhanced imaging, when available. Demographic information, relevant clinical history, laboratory results, and radiology report information may also be collected. Pathological findings will serve as the reference standard for model training and evaluation. All data will be de-identified before processing and analysis.

The study will develop artificial intelligence models for the following tasks:

  1. Detection and localization of renal tumors on multisequence MRI.
  2. Segmentation of renal tumors and extraction of quantitative imaging features.
  3. Classification of common benign and malignant renal tumor subtypes.
  4. Identification of rare pathological subtypes using small-sample or cross-modal learning methods.
  5. Prediction of histological grade for malignant renal tumors.
  6. Integration of MRI, demographic, clinical, and laboratory information to improve pathological subtyping and grading.

The dataset will be divided into model-development and validation datasets. Additional cases collected from different time periods or participating sources may be used for independent testing. Model performance will be evaluated by comparing artificial intelligence predictions with pathological diagnoses.

This study does not assign any treatment or diagnostic intervention. It will not affect participants' routine clinical care and will not require additional imaging examinations, blood collection, surgery, medication, or follow-up visits. The study is intended to develop an intelligent MRI-based diagnostic system that may support preoperative decision-making for patients with renal tumors.

Studietype

Observationeel

Inschrijving (Geschat)

900

Contacten en locaties

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Studiecontact

Studie Locaties

  • China
      • Beijing, China
        • Werving
        • Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College
        • Contact:

Deelname Criteria

Onderzoekers zoeken naar mensen die aan een bepaalde beschrijving voldoen, de zogenaamde geschiktheidscriteria. Enkele voorbeelden van deze criteria zijn iemands algemene gezondheidstoestand of eerdere behandelingen.

Geschiktheidscriteria

Leeftijden die in aanmerking komen voor studie

  • Volwassen
  • Oudere volwassene

Accepteert gezonde vrijwilligers

Nee

Bemonsteringsmethode

Niet-waarschijnlijkheidssteekproef

Studie Bevolking

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.

Beschrijving

Inclusion Criteria:

  • 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.

Exclusion Criteria:

  • 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.

Studie plan

Dit gedeelte bevat details van het studieplan, inclusief hoe de studie is opgezet en wat de studie meet.

Hoe is de studie opgezet?

Ontwerpdetails

Cohorten en interventies

Groep / Cohort
Interventie / Behandeling
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.
Diagnostische toets: 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.

Wat meet het onderzoek?

Primaire uitkomstmaten

Uitkomstmaat
Maatregel Beschrijving
Tijdsspanne
Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
Tijdsspanne: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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
Tijdsspanne: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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.

Secundaire uitkomstmaten

Uitkomstmaat
Maatregel Beschrijving
Tijdsspanne
Performance of the Artificial Intelligence Model for Renal Tumor Detection
Tijdsspanne: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
The ability of the artificial intelligence model to detect and localize renal tumors on multisequence MRI will be evaluated by comparing model-generated tumor locations with expert manual annotations. Detection performance will be summarized using sensitivity and the proportion of correctly localized renal tumors.
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
Tijdsspanne: At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
Artificial intelligence-generated renal tumor segmentations will be compared with pixel-level manual annotations prepared under the supervision of experienced physicians. Segmentation agreement will be evaluated using the Dice similarity coefficient.
At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.

Medewerkers en onderzoekers

Hier vindt u mensen en organisaties die betrokken zijn bij dit onderzoek.

Sponsor

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Medewerkers

Shanxi Province Cancer Hospital
Peking University People's Hospital
Chinese PLA General Hospital
RenJi Hospital
Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center

Studie record data

Deze datums volgen de voortgang van het onderzoeksdossier en de samenvatting van de ingediende resultaten bij ClinicalTrials.gov. Studieverslagen en gerapporteerde resultaten worden beoordeeld door de National Library of Medicine (NLM) om er zeker van te zijn dat ze voldoen aan specifieke kwaliteitscontrolenormen voordat ze op de openbare website worden geplaatst.

Bestudeer belangrijke data

Studie start (Werkelijk)

1 januari 2021

Primaire voltooiing (Geschat)

31 december 2026

Studie voltooiing (Geschat)

31 december 2026

Studieregistratiedata

Eerst ingediend

29 juli 2026

Eerst ingediend dat voldeed aan de QC-criteria

29 juli 2026

Eerst geplaatst (Werkelijk)

4 augustus 2026

Updates van studierecords

Laatste update geplaatst (Werkelijk)

4 augustus 2026

Laatste update ingediend die voldeed aan QC-criteria

29 juli 2026

Laatst geverifieerd

1 juli 2026

Meer informatie

Termen gerelateerd aan deze studie

Trefwoorden

Aanvullende relevante MeSH-voorwaarden

Andere studie-ID-nummers

  • NCC-020738

Plan Individuele Deelnemersgegevens (IPD)

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NEE

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