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Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
- Source :
- Korean Journal of Radiology
- Publication Year :
- 2019
-
Abstract
- 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.
- Subjects :
- Treatment response
medicine.medical_specialty
Guidelines as Topic
Review Article
030218 nuclear medicine & medical imaging
Diagnosis, Differential
Neuroimaging and Head & Neck
03 medical and health sciences
0302 clinical medicine
Imaging, Three-Dimensional
Radiomics
Clinical workflow
Neuro-oncology
Medicine
Humans
Radiology, Nuclear Medicine and imaging
Routine clinical practice
Medical physics
Physiologic Imaging
Evidence-Based Medicine
business.industry
Brain Neoplasms
Deep learning
Optical Imaging
Prognosis
Clinical trial
Clinical Practice
Workflow
030220 oncology & carcinogenesis
Artificial intelligence
business
Subjects
Details
- ISSN :
- 20058330
- Volume :
- 21
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- Korean journal of radiology
- Accession number :
- edsair.doi.dedup.....dd1c25fab250c2ba4409c4c5e3fd4201