An MRI-Based Study of Intelligent Pathological Subtyping and Grading of Renal Tumors
調査の概要
詳細な説明
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:
- Detection and localization of renal tumors on multisequence MRI.
- Segmentation of renal tumors and extraction of quantitative imaging features.
- Classification of common benign and malignant renal tumor subtypes.
- Identification of rare pathological subtypes using small-sample or cross-modal learning methods.
- Prediction of histological grade for malignant renal tumors.
- 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.
研究の種類
入学 (推定)
連絡先と場所
研究連絡先
- 名前：Xiongjun Ye
- 電話番号：010-87787170 010-87787170
- メール：yexiongjun@cicams.ac.cn
研究場所
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Beijing、中国
- 募集
- Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College
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コンタクト：
- xiongjun YE
- 電話番号：010-87787170
- メール：yexiongjun@cicams.ac.cn
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-
参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
介入・治療 |
|---|---|
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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.
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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.
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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
時間枠：At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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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.
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At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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Accuracy of MRI-Based Artificial Intelligence for Histological Grading of Malignant Renal Tumors
時間枠：At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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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.
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At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Performance of the Artificial Intelligence Model for Renal Tumor Detection
時間枠：At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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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.
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At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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Accuracy of Artificial Intelligence-Based Renal Tumor Segmentation
時間枠：At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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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.
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At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
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協力者と研究者
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
追加の関連 MeSH 用語
その他の研究ID番号
- NCC-020738
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