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Theranostics 2019; 9(5):1303-1322. doi:10.7150/thno.30309 This issue Cite

Review

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

Zhenyu Liu^1,5*, Shuo Wang^1,5*, Di Dong^1,5*, Jingwei Wei^1,5*, Cheng Fang^3*, Xuezhi Zhou^1,4, Kai Sun^1,4, Longfei Li^1,6, Bo Li^3✉, Meiyun Wang^2✉, Jie Tian^1,4,7✉

1. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
2. Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
3. Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
4. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
5. University of Chinese Academy of Sciences, Beijing, 100080, China
6. Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, 450052, China
7. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
*These authors contributed equally to this work.

✉ Corresponding authors: Jie Tian, Address: Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. E-mail: jie.tianac.cn or Bo Li, Address: Department of Hepatobiliary Surgery, the Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China. E-mail: liboer2002com or Meiyun Wang, Address: Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China. E-mail: marian9999com More

Abstract

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.

Keywords: radiomics, medical imaging, precision diagnosis and treatment, oncology

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