Artificial intelligence(AI)is transforming the diagnostic landscape of malignant tumors in the urinary system,including prostate cancer,bladder cancer,and renal cell carcinoma(RCC).By integrating imaging,pathology,and...Artificial intelligence(AI)is transforming the diagnostic landscape of malignant tumors in the urinary system,including prostate cancer,bladder cancer,and renal cell carcinoma(RCC).By integrating imaging,pathology,and molecular data,AI enhances the precision and reproducibility of tumor detection,grading,and risk stratification.In prostate cancer,AI-assisted multiparametric Magnetic resonance imaging(MRI)and digital pathology systems improve lesion localization and Gleason scoring.For bladder cancer,deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging(CT/MRI)enable real-time lesion segmentation and non-invasive biomarker prediction,such as Programmed Cell Death-Ligand 1(PD-L1)expression.In RCC,AI,combined with CT/MRI and multi-omics data,aids in subtype classification and prognostic prediction,supporting personalized therapy.However,despite these promising advances,challenges such as data standardization,model generalizability,interpretability,and regulatory compliance hinder AI’s clinical translation.This review outlines the current state of AI in urological cancer diagnosis and prognosis,its technological innovations,and the clinical challenges and opportunities that lie ahead.展开更多
基金supported by grants from the Hangzhou Key Project for Agricultural and Social Development under Grant No.20231203A12(JZ)the General Program of the Scientific Research Special Project for Post-Marketing Clinical Research of Innovative Drugs,Development Center for Medical Science&Technology,National Health Commission of the People’s Republic of China under Grant No.WKZX2024CX104202(JZ).
文摘Artificial intelligence(AI)is transforming the diagnostic landscape of malignant tumors in the urinary system,including prostate cancer,bladder cancer,and renal cell carcinoma(RCC).By integrating imaging,pathology,and molecular data,AI enhances the precision and reproducibility of tumor detection,grading,and risk stratification.In prostate cancer,AI-assisted multiparametric Magnetic resonance imaging(MRI)and digital pathology systems improve lesion localization and Gleason scoring.For bladder cancer,deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging(CT/MRI)enable real-time lesion segmentation and non-invasive biomarker prediction,such as Programmed Cell Death-Ligand 1(PD-L1)expression.In RCC,AI,combined with CT/MRI and multi-omics data,aids in subtype classification and prognostic prediction,supporting personalized therapy.However,despite these promising advances,challenges such as data standardization,model generalizability,interpretability,and regulatory compliance hinder AI’s clinical translation.This review outlines the current state of AI in urological cancer diagnosis and prognosis,its technological innovations,and the clinical challenges and opportunities that lie ahead.
