CT Radiomics in Thoracic Oncology: Technique and Clinical Applications
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Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.
KeywordsComputed tomography Lung cancer Image processing Biomarkers
Compliance with Ethical Standards
Conflict of Interest
Geewon Lee, So Hyeon Bak, and Ho Yun Lee declare that they have no conflict of interest. This research was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare (HI17C0086) and by National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIP; Ministry of Science, ICT, & Future Planning) (No. NRF-2016R1A2B4013046 and NRF-2017M2A2A7A02018568).
This article does not contain any studies with human participants or animals performed by any of the authors.
Requirement to obtain informed consent was waived.
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