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Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review

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dc.contributor.authorWei Du
dc.contributor.authorLin Mei
dc.contributor.authorShunsuke Koga
dc.contributor.authorRanjitha Pratap Nair
dc.contributor.departmentBiología Celular e Histología
dc.contributor.editorUniversidad de Murcia, Departamento de Biologia Celular e Histiologia
dc.date.accessioned2026-09-02T15:43:56Z
dc.date.available2026-09-02T15:43:56Z
dc.date.issued2026
dc.description.abstractArtificial 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.
dc.formatapplication/pdf
dc.format.extent14
dc.identifier.doihttps://doi.org/10.14670/HH-25-059
dc.identifier.eissn1699-5848
dc.identifier.issn0213-3911
dc.identifier.urihttps://hdl.handle.net/10201/248421
dc.languageeng
dc.relationSin financiación externa a la Universidad
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectComputational pathology
dc.subjectWhole-slide imaging
dc.subjectProstate cancer
dc.subjectGleason grading
dc.subjectTransformer
dc.subjectMultiple instance learning
dc.subjectMultimodal models
dc.subjectClinical implementation
dc.subjectLarge language models
dc.subjectDeep learning
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleArtificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review
dc.typeinfo:eu-repo/semantics/article
dspace.entity.typePublication
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Histology and histopathology, Vol.41, Nº 9, (2026)