Publication: Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review
Authors
Wei Du ; Lin Mei ; Shunsuke Koga ; Ranjitha Pratap Nair
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Publisher
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Universidad de Murcia, Departamento de Biologia Celular e Histiologia
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DOI
https://doi.org/10.14670/HH-25-059
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info:eu-repo/semantics/article
Description
Abstract
Artificial intelligence (AI) has been
transforming many aspects of medical care. In prostate
cancer, ongoing progress in AI has improved research
and patient care. Recent advances in machine learning
and deep learning have produced tools that help diagnose
cancer, assess risk, and predict outcomes. In screening,
AI-based risk calculators improve detection and help
avoid unnecessary biopsies. Deep learning algorithms,
particularly convolutional neural networks, have
demonstrated expert-level performance in pathology,
identifying malignancy and assigning Gleason grades
with high accuracy. These tools also streamline
workflow, flagging challenging cases for review and
quantifying prognostic markers, such as Ki-67 and
cribriform patterns. In addition, AI-based models can
predict molecular alterations, microsatellite instability,
and lymph node metastasis directly from histology
images, providing cost-effective alternatives to traditional
assays. The development of multimodal models
integrates digital pathology and clinical parameters,
enabling personalized treatment recommendations and
improved outcome prediction. Natural language
processing and large language models further expand
AI’s potential, facilitating information extraction from
clinical notes and enhancing patient education. Despite
these advances, most studies remain retrospective with
heterogeneous endpoints. Performance often drops when
models are tested at new sites because of differences in
patient populations and slide preparation. Access to large,
well-annotated datasets is limited, and technical variation
hampers reproducibility. To move toward clinical use, the
field needs prospective, multicenter validation,
preanalytical and analytical standardization, and clear
reporting of failure modes and human oversight. Emerging approaches, including self-supervised
pretraining, transformer-based image models, and
language-vision systems, are likely to improve
generalization and support more personalized care.
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