Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review.

Autor: Jensen MP; Pathology Department, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK.; Briscoe Lab, The Francis Crick Institute, London, UK., Qiang Z; School of Medicine and Population Health, University of Sheffield Medical School, Sheffield, UK., Khan DZ; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK.; Department of Computer Science, University College London, London, UK., Stoyanov D; Department of Computer Science, University College London, London, UK., Baldeweg SE; Department of Diabetes and Endocrinology, University College London Hospitals, London, UK.; Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London, UK., Jaunmuktane Z; Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK.; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK.; Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK., Brandner S; Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK.; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK., Marcus HJ; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK.; Department of Computer Science, University College London, London, UK.
Jazyk: angličtina
Zdroj: Neuropathology and applied neurobiology [Neuropathol Appl Neurobiol] 2024 Jun; Vol. 50 (3), pp. e12981.
DOI: 10.1111/nan.12981
Abstrakt: The convergence of digital pathology and artificial intelligence could assist histopathology image analysis by providing tools for rapid, automated morphological analysis. This systematic review explores the use of artificial intelligence for histopathological image analysis of digitised central nervous system (CNS) tumour slides. Comprehensive searches were conducted across EMBASE, Medline and the Cochrane Library up to June 2023 using relevant keywords. Sixty-eight suitable studies were identified and qualitatively analysed. The risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST) criteria. All the studies were retrospective and preclinical. Gliomas were the most frequently analysed tumour type. The majority of studies used convolutional neural networks or support vector machines, and the most common goal of the model was for tumour classification and/or grading from haematoxylin and eosin-stained slides. The majority of studies were conducted when legacy World Health Organisation (WHO) classifications were in place, which at the time relied predominantly on histological (morphological) features but have since been superseded by molecular advances. Overall, there was a high risk of bias in all studies analysed. Persistent issues included inadequate transparency in reporting the number of patients and/or images within the model development and testing cohorts, absence of external validation, and insufficient recognition of batch effects in multi-institutional datasets. Based on these findings, we outline practical recommendations for future work including a framework for clinical implementation, in particular, better informing the artificial intelligence community of the needs of the neuropathologist.
(© 2024 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society.)
Databáze: MEDLINE
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