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Home > Issues > RetrieveRadiomics in Oncology: Method and Clinical Applications
Journal of Magnetics, Volume 25, Number 1, 31 Mar 2020, Pages 110-116
Seung Hwan Lee (Department of Surgery, Kyung Hee University Hospital at Gangdong, 892, Dongnam-ro, Gangdong, Seoul 05278, Republic of Korea), Cheon Woong Choi (Department of Pulmonary and Critical Care Medicine, Kyung Hee University Hospital at Gangdong, 892, Dongnam-ro,), Gab-Jung Kim (Department of Radiological Techonology, Songho University, 210, Namsan-ro, Hoengseong, Gangwon 25242, Republic of Korea), Cheol-Soo Park (Department of Radiological Science, Hallym Polytechnic University, 48, Janghak-gil, Dong-myeon, Chuncheon,), Yong-Soo Han (Department of Radiological Science, Hallym Polytechnic University, 48, Janghak-gil, Dong-myeon, Chuncheon,), Sang Won Park * 
(Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon,), Se-Jong, Yoo * (Department of Radiological Technology, Daejeon Health Institute of Technology, 21,Chungjeong-ro, Dong-gu,)
(Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon,), Se-Jong, Yoo * (Department of Radiological Technology, Daejeon Health Institute of Technology, 21,Chungjeong-ro, Dong-gu,)
Abstract
Radiomics is a field for quantitative image assessment and research using a variety of sophisticated and complex
algorithms by extracting a significant and numerous amount of specific functional elements (called texture
feature) from medical images. It is mainly used in oncology and can provide a variety of capabilities in a noninvasive
way, such as explaining the tumor phenotype, monitoring the response to treatment, comparing with
normal tissue, diagnosing the prognosis of the patient and predicting survival. In this study, we will introduce of
the workflow of radiomics and discuss the types of texture features, application of clinical cases, and the prospects
of radiomics.
algorithms by extracting a significant and numerous amount of specific functional elements (called texture
feature) from medical images. It is mainly used in oncology and can provide a variety of capabilities in a noninvasive
way, such as explaining the tumor phenotype, monitoring the response to treatment, comparing with
normal tissue, diagnosing the prognosis of the patient and predicting survival. In this study, we will introduce of
the workflow of radiomics and discuss the types of texture features, application of clinical cases, and the prospects
of radiomics.
Keywords: radiomics; medical image; magnetic resonance imaging; fMRI; oncology; diagnostic markers
DOI: https://doi.org/10.4283/JMAG.2020.25.1.110
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