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Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-onc...

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Detalles Bibliográficos
Autores principales: Park, Ji Eun, Kickingereder, Philipp, Kim, Ho Sung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7458866/
https://www.ncbi.nlm.nih.gov/pubmed/32729271
http://dx.doi.org/10.3348/kjr.2019.0847
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author Park, Ji Eun
Kickingereder, Philipp
Kim, Ho Sung
author_facet Park, Ji Eun
Kickingereder, Philipp
Kim, Ho Sung
author_sort Park, Ji Eun
collection PubMed
description Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice.
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spelling pubmed-74588662020-10-01 Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging Park, Ji Eun Kickingereder, Philipp Kim, Ho Sung Korean J Radiol Neuroimaging and Head & Neck Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice. The Korean Society of Radiology 2020-10 2020-07-27 /pmc/articles/PMC7458866/ /pubmed/32729271 http://dx.doi.org/10.3348/kjr.2019.0847 Text en Copyright © 2020 The Korean Society of Radiology http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Neuroimaging and Head & Neck
Park, Ji Eun
Kickingereder, Philipp
Kim, Ho Sung
Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
title Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
title_full Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
title_fullStr Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
title_full_unstemmed Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
title_short Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
title_sort radiomics and deep learning from research to clinical workflow: neuro-oncologic imaging
topic Neuroimaging and Head & Neck
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7458866/
https://www.ncbi.nlm.nih.gov/pubmed/32729271
http://dx.doi.org/10.3348/kjr.2019.0847
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