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

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....

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Bibliographic Details
Published in:Diagnostics (Basel) 2021-08, Vol.11 (8), p.1406
Main Authors: Murchan, Pierre, Ó'Brien, Cathal, O'Connell, Shane, McNevin, Ciara S, Baird, Anne-Marie, Sheils, Orla, Ó Broin, Pilib, Finn, Stephen P
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Back propagation
Biomarkers
cancer
Cancer therapies
Datasets
Deep learning
Gene expression
histopathology
Kinases
Lung cancer
Machine learning
Medical prognosis
Melanoma
molecular diagnostics
Mutation
Neural networks
Quality of life
Review
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Summary: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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics11081406