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Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats
Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6482447/ https://www.ncbi.nlm.nih.gov/pubmed/30383900 http://dx.doi.org/10.1002/cncr.31630 |
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author | Napel, Sandy Mu, Wei Jardim‐Perassi, Bruna V. Aerts, Hugo J. W. L. Gillies, Robert J. |
author_facet | Napel, Sandy Mu, Wei Jardim‐Perassi, Bruna V. Aerts, Hugo J. W. L. Gillies, Robert J. |
author_sort | Napel, Sandy |
collection | PubMed |
description | Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology. |
format | Online Article Text |
id | pubmed-6482447 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64824472019-04-25 Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats Napel, Sandy Mu, Wei Jardim‐Perassi, Bruna V. Aerts, Hugo J. W. L. Gillies, Robert J. Cancer Review Articles Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology. John Wiley and Sons Inc. 2018-11-01 2018-12-15 /pmc/articles/PMC6482447/ /pubmed/30383900 http://dx.doi.org/10.1002/cncr.31630 Text en © 2018 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Review Articles Napel, Sandy Mu, Wei Jardim‐Perassi, Bruna V. Aerts, Hugo J. W. L. Gillies, Robert J. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats |
title | Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats |
title_full | Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats |
title_fullStr | Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats |
title_full_unstemmed | Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats |
title_short | Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats |
title_sort | quantitative imaging of cancer in the postgenomic era: radio(geno)mics, deep learning, and habitats |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6482447/ https://www.ncbi.nlm.nih.gov/pubmed/30383900 http://dx.doi.org/10.1002/cncr.31630 |
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