The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted the...

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Vydané v:Theranostics Ročník 9; číslo 5; s. 1303 - 1322
Hlavní autori: Liu, Zhenyu, Wang, Shuo, Dong, Di, Wei, Jingwei, Fang, Cheng, Zhou, Xuezhi, Sun, Kai, Li, Longfei, Li, Bo, Wang, Meiyun, Tian, Jie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Australia Ivyspring International Publisher 01.01.2019
Predmet:
Diagnostic Imaging - methods
Diagnostic Imaging - trends
Humans
Image Processing, Computer-Assisted - methods
Image Processing, Computer-Assisted - trends
Neoplasms - diagnostic imaging
Precision Medicine - methods
Precision Medicine - trends
Radiography - methods
Radiography - trends
Review
radiomics
precision diagnosis and treatment
medical imaging
oncology
ISSN:1838-7640, 1838-7640
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Shrnutí:Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.
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These authors contributed equally to this work.
Competing Interests: The authors have declared that no competing interest exists.
ISSN:1838-7640
1838-7640
DOI:10.7150/thno.30309