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

Kidney Neoplasm, Renal Tumor

Magnetic Resonance Imaging, Artificial Intelligence, Deep Learning, Renal Tumor, Pathological Subtyping, Histological Grading, Multimodal Learning, Computer-Aided Diagnosis, Tumor Segmentation, Rare Renal Tumor Subtypes

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.

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.

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