ReviewBladder cancer (Amsterdam, Netherlands)
Computational pathology in bladder cancer: A scoping review.
Review in Bladder cancer (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
Funding
Abstract
Background: Histologic assessment of tumor tissue is fundamental to the diagnosis and management of bladder cancer. As digital pathology becomes more widely adopted and artificial intelligence (AI) tools rapidly advance, computational pathology has emerged as a promising avenue for extracting clinically meaningful information from pathology images. Objectives: This scoping review aims to provide a comprehensive overview of the current landscape of computational pathology in bladder cancer. We describe the commonly used AI and machine learning approaches for analyzing whole slide images (WSIs) and summarize their use in diagnosis, grading, staging, molecular classification, prognostication, and treatment response prediction in bladder cancer. Methods: This review was conducted in accordance with PRISMA-ScR 2018 guidelines. We searched PubMed/MEDLINE for primary research articles focused on computational or AI-based analysis of human bladder cancer histopathology images across predefined predictive tasks. Eligible studies underwent full-text review. Findings were synthesized narratively by task. Results: A total of 2055 abstracts were screened. Fifty-three studies met eligibility criteria and were included in the review. Conclusions: Computational pathology offers substantial promise for augmenting pathologist workflows and advancing personalized care in bladder cancer. Current studies remain largely retrospective, but there is a rapid pace of innovation and a clear opportunity to work towards real-world clinical implementation. Establishing shared multi-institutional datasets and standardized evaluation frameworks could help support future research and expedite clinical implementation of computational pathology tools for bladder cancer.
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