Journal Article (Review Article)

DKFZ-2026-00552

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.

Zhan, M. ; Zhou, Z. ; Zhang, J. ; Wang, X. ; Li, C. ; Pan, B. ; Luo, Z. ; Shi, W. ; Wang, Y.DKFZ* ; Li, M. ; Wang, W. ; Shi, R. ; Zhu, J.

2026
[Verlag nicht ermittelbar] St. Laurent, Quebec

Abstract: 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.

Keyword(s): Humans (MeSH) ; Artificial Intelligence (MeSH) ; Prognosis (MeSH) ; Urologic Neoplasms: diagnosis (MeSH) ; Urologic Neoplasms: diagnostic imaging (MeSH) ; Forecasting (MeSH) ; Prostatic Neoplasms: diagnosis (MeSH) ; Urinary Bladder Neoplasms: diagnosis (MeSH) ; Artificial intelligence ; bladder cancer ; multimodal AI ; prostate cancer ; renal cell carcinoma ; urologic cancers

Note: Proteomics and Cancer Cell Signaling Group, German Cancer Research Center, Heidelberg, Germany.

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Contributing Institute(s):

1. KKE Pädiatrische Leukämie (A400)

Research Program(s):

1. 311 - Zellbiologie und Tumorbiologie (POF4-311) (POF4-311)

Appears in the scientific report 2026

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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