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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Bibliographic Details
Published in:Korean journal of radiology 2020, 21(10), , pp.1126-1137
Main Authors: Park, Ji Eun, Kickingereder, Philipp, Kim, Ho Sung
Format: Article
Language:English
Subjects:
Algorithms
Biomarkers
Brain cancer
Brain Neoplasms - diagnosis
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - therapy
Brain research
Deep Learning
Diagnosis, Differential
Evidence-Based Medicine
Feature selection
Guidelines as Topic
Humans
Imaging, Three-Dimensional
Machine learning
Magnetic resonance imaging
Medical prognosis
Neural networks
Neuroimaging and Head & Neck
Oncology
Optical Imaging - methods
Pattern recognition
Prognosis
Tumors
방사선과학
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Summary: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.
ISSN:1229-6929
2005-8330
DOI:10.3348/kjr.2019.0847