An MRI-Based Study of Intelligent Pathological Subtyping and Grading of Renal Tumors
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 900
- 试验地点
- 1
- 主要终点
- Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
时间窗: 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.
Accuracy of MRI-Based Artificial Intelligence for Histological Grading of Malignant Renal Tumors
时间窗: 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.
次要结局
- Performance of the Artificial Intelligence Model for Renal Tumor Detection(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(At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.)
研究者
Xiongjun Ye
Chief Physician
Cancer Institute and Hospital, Chinese Academy of Medical Sciences