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

29 lipca 2026 zaktualizowane przez: 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.

Przegląd badań

Status

Rekrutacyjny

Warunki

Interwencja / Leczenie

Szczegółowy opis

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.

Typ studiów

Obserwacyjny

Zapisy (Szacowany)

900

Kontakty i lokalizacje

Ta sekcja zawiera dane kontaktowe osób prowadzących badanie oraz informacje o tym, gdzie badanie jest przeprowadzane.

Kontakt w sprawie studiów

Lokalizacje studiów

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

Kryteria uczestnictwa

Badacze szukają osób, które pasują do określonego opisu, zwanego kryteriami kwalifikacyjnymi. Niektóre przykłady tych kryteriów to ogólny stan zdrowia danej osoby lub wcześniejsze leczenie.

Kryteria kwalifikacji

Wiek uprawniający do nauki

  • Dorosły
  • Starszy dorosły

Akceptuje zdrowych ochotników

Nie

Metoda próbkowania

Próbka bez prawdopodobieństwa

Badana populacja

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.

Opis

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 studiów

Ta sekcja zawiera szczegółowe informacje na temat planu badania, w tym sposób zaprojektowania badania i jego pomiary.

Jak projektuje się badanie?

Szczegóły projektu

Kohorty i interwencje

Grupa / Kohorta
Interwencja / Leczenie
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 diagnostyczny: 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.

Co mierzy badanie?

Podstawowe miary wyniku

Miara wyniku
Opis środka
Ramy czasowe
Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
Ramy czasowe: 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
Ramy czasowe: 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.

Miary wyników drugorzędnych

Miara wyniku
Opis środka
Ramy czasowe
Performance of the Artificial Intelligence Model for Renal Tumor Detection
Ramy czasowe: 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
Ramy czasowe: 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.

Współpracownicy i badacze

Tutaj znajdziesz osoby i organizacje zaangażowane w to badanie.

Sponsor

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Współpracownicy

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

Daty zapisu na studia

Daty te śledzą postęp w przesyłaniu rekordów badań i podsumowań wyników do ClinicalTrials.gov. Zapisy badań i zgłoszone wyniki są przeglądane przez National Library of Medicine (NLM), aby upewnić się, że spełniają określone standardy kontroli jakości, zanim zostaną opublikowane na publicznej stronie internetowej.

Główne daty studiów

Rozpoczęcie studiów (Rzeczywisty)

1 stycznia 2021

Zakończenie podstawowe (Szacowany)

31 grudnia 2026

Ukończenie studiów (Szacowany)

31 grudnia 2026

Daty rejestracji na studia

Pierwszy przesłany

29 lipca 2026

Pierwszy przesłany, który spełnia kryteria kontroli jakości

29 lipca 2026

Pierwszy wysłany (Rzeczywisty)

4 sierpnia 2026

Aktualizacje rekordów badań

Ostatnia wysłana aktualizacja (Rzeczywisty)

4 sierpnia 2026

Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości

29 lipca 2026

Ostatnia weryfikacja

1 lipca 2026

Więcej informacji

Terminy związane z tym badaniem

Słowa kluczowe

Dodatkowe istotne warunki MeSH

Inne numery identyfikacyjne badania

  • NCC-020738

Plan dla danych uczestnika indywidualnego (IPD)

Planujesz udostępniać dane poszczególnych uczestników (IPD)?

NIE

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