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Review

Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics

1
Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland
2
Department of Histopathology, St James’s Hospital, P.O. Box 580, James’s Street, D08 X4RX Dublin, Ireland
3
School of Mathematics, Statistics, and Applied Mathematics, National University of Ireland Galway, H91 TK33 Galway, Ireland
4
Department of Medical Oncology, St James’s Hospital, D08 NHY1 Dublin, Ireland
5
School of Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, D02 A440 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Dechan Chen
Diagnostics 2021, 11(8), 1406; https://doi.org/10.3390/diagnostics11081406
Received: 2 July 2021 / Revised: 24 July 2021 / Accepted: 27 July 2021 / Published: 3 August 2021
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning. View Full-Text
Keywords: histopathology; deep learning; cancer; molecular diagnostics histopathology; deep learning; cancer; molecular diagnostics
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MDPI and ACS Style

Murchan, P.; Ó’Brien, C.; O’Connell, S.; McNevin, C.S.; Baird, A.-M.; Sheils, O.; Ó Broin, P.; Finn, S.P. Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics. Diagnostics 2021, 11, 1406. https://doi.org/10.3390/diagnostics11081406

AMA Style

Murchan P, Ó’Brien C, O’Connell S, McNevin CS, Baird A-M, Sheils O, Ó Broin P, Finn SP. Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics. Diagnostics. 2021; 11(8):1406. https://doi.org/10.3390/diagnostics11081406

Chicago/Turabian Style

Murchan, Pierre, Cathal Ó’Brien, Shane O’Connell, Ciara S. McNevin, Anne-Marie Baird, Orla Sheils, Pilib Ó Broin, and Stephen P. Finn. 2021. "Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics" Diagnostics 11, no. 8: 1406. https://doi.org/10.3390/diagnostics11081406

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