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Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics
- Source :
- Diagnostics, Vol 11, Iss 1406, p 1406 (2021), Diagnostics
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
Medicine (General)
medicine.medical_specialty
Treatment response
Clinical Biochemistry
Review
molecular diagnostics
03 medical and health sciences
R5-920
0302 clinical medicine
Clinical decision making
Molecular genetics
cancer
Medicine
Medical physics
business.industry
Deep learning
deep learning
Cancer
Molecular diagnostics
medicine.disease
Triage
Cancer treatment
030104 developmental biology
030220 oncology & carcinogenesis
histopathology
Artificial intelligence
business
Subjects
Details
- ISSN :
- 20754418
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Diagnostics
- Accession number :
- edsair.doi.dedup.....2423fb1220a423ac2f04c1c43b768d43