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

29. juli 2026 opdateret af: 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.

Studieoversigt

Status

Rekruttering

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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.

Undersøgelsestype

Observationel

Tilmelding (Anslået)

900

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

Studiesteder

  • Kina
      • Beijing, Kina
        • Rekruttering
        • Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College
        • Kontakt:

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

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.

Beskrivelse

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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
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.
Diagnostisk 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.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
Tidsramme: 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
Tidsramme: 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.

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Performance of the Artificial Intelligence Model for Renal Tumor Detection
Tidsramme: 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
Tidsramme: 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.

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Samarbejdspartnere

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

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. januar 2021

Primær færdiggørelse (Anslået)

31. december 2026

Studieafslutning (Anslået)

31. december 2026

Datoer for studieregistrering

Først indsendt

29. juli 2026

Først indsendt, der opfyldte QC-kriterier

29. juli 2026

Først opslået (Faktiske)

4. august 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

4. august 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

29. juli 2026

Sidst verificeret

1. juli 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • NCC-020738

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

INGEN

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