|
KMID : 0880420200210101126
|
|
Korean Journal of Radiology
2020 Volume.21 No. 10 p.1126 ~ p.1137
|
|
|
Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
|
|
Park Ji-Eun
Kickingereder Philipp
Kim Ho-Sung
|
|
|
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.
|
|
|
KEYWORD
|
|
|
Radiomics, Deep learning, Neuro-oncology, Clinical workflow
|
|
|
FullTexts / Linksout information
|
|
 
|
|
|
Listed journal information
|
|
    
|