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

29 juillet 2026 mis à jour par: 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.

Aperçu de l'étude

Statut

Recrutement

Les conditions

Intervention / Traitement

Description détaillée

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.

Type d'étude

Observationnel

Inscription (Estimé)

900

Contacts et emplacements

Cette section fournit les coordonnées de ceux qui mènent l'étude et des informations sur le lieu où cette étude est menée.

Coordonnées de l'étude

Lieux d'étude

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

Critères de participation

Les chercheurs recherchent des personnes qui correspondent à une certaine description, appelée critères d'éligibilité. Certains exemples de ces critères sont l'état de santé général d'une personne ou des traitements antérieurs.

Critère d'éligibilité

Âges éligibles pour étudier

  • Adulte
  • Adulte plus âgé

Accepte les volontaires sains

Non

Méthode d'échantillonnage

Échantillon non probabiliste

Population étudiée

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.

La description

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.

Plan d'étude

Cette section fournit des détails sur le plan d'étude, y compris la façon dont l'étude est conçue et ce que l'étude mesure.

Comment l'étude est-elle conçue ?

Détails de conception

Cohortes et interventions

Groupe / Cohorte
Intervention / Traitement
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.
Test diagnostique: 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.

Que mesure l'étude ?

Principaux critères de jugement

Mesure des résultats
Description de la mesure
Délai
Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
Délai: 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
Délai: 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.

Mesures de résultats secondaires

Mesure des résultats
Description de la mesure
Délai
Performance of the Artificial Intelligence Model for Renal Tumor Detection
Délai: 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
Délai: 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.

Collaborateurs et enquêteurs

C'est ici que vous trouverez les personnes et les organisations impliquées dans cette étude.

Parrainer

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Collaborateurs

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

Dates d'enregistrement des études

Ces dates suivent la progression des dossiers d'étude et des soumissions de résultats sommaires à ClinicalTrials.gov. Les dossiers d'étude et les résultats rapportés sont examinés par la Bibliothèque nationale de médecine (NLM) pour s'assurer qu'ils répondent à des normes de contrôle de qualité spécifiques avant d'être publiés sur le site Web public.

Dates principales de l'étude

Début de l'étude (Réel)

1 janvier 2021

Achèvement primaire (Estimé)

31 décembre 2026

Achèvement de l'étude (Estimé)

31 décembre 2026

Dates d'inscription aux études

Première soumission

29 juillet 2026

Première soumission répondant aux critères de contrôle qualité

29 juillet 2026

Première publication (Réel)

4 août 2026

Mises à jour des dossiers d'étude

Dernière mise à jour publiée (Réel)

4 août 2026

Dernière mise à jour soumise répondant aux critères de contrôle qualité

29 juillet 2026

Dernière vérification

1 juillet 2026

Plus d'information

Termes liés à cette étude

Mots clés

Termes MeSH pertinents supplémentaires

Autres numéros d'identification d'étude

  • NCC-020738

Plan pour les données individuelles des participants (IPD)

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