Radiomics and imaging genomics in precision medicine

REVIEW ARTICLE Precision and Future Medicine 2017;1(1):10-31 https://doi.org/10.23838/pfm.2017.00101 pISSN: 2508-7940 · eISSN: 2508-7959 Radiomics and imaging genomics in precision medicine Geewon Lee1,2, Ho Yun Lee1, Eun Sook Ko1, Woo Kyoung Jeong1 Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of 1 Medicine, Seoul, Korea Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School 2 of Medicine, Busan, Korea Received: February 3, 2017 Revised: February 18, 2017 Accepted: February 24, 2017 ABSTRACT Corresponding author: Ho Yun Lee Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea Tel: +82-2-3410-2502 E-mail: Keywords: Imaging genomics; Neoplasms; Radiomics “Radiomics,” a field of study in which high-throughput data is extracted and large amo unts of advanced quantitative imaging features are analyzed from medical images, and “imaging genomics,” the field of study of high-throughput methods of associating imaging features with genomic data, has gathered academic interest. However, a radiomics and imaging genomics approach in the oncology world is still in its very early stages and many problems remain to be solved. In this review, we will look through the steps of radiomics and imaging genomics in oncology, specifically addressing potential applications in each organ and focusing on technical issues. INTRODUCTION Medical imaging such as computed tomography (CT), positron emission tomography (PET), or magnetic resonance imaging (MRI) is mandatory in the diagnosis, staging, treatment planning, postoperative surveillance, and response evaluation in the routine management of cancer. Although these conventional modalities provide important information on cancer phenotypes, yet a great deal of genetic and prognostic information remains unrevealed. Recently, there is universal understanding that genomic heterogeneity exists among and even within tumors and that those differences can play an important role in determining the likelihood of a clinical response to treatment with particular agents [1-4]. In other words, the success of precision medicine requires a clear understanding of each patient’s tumoral heteroThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/ by-nc/4.0/). geneity and individual situation. Here, “radiomics,” a field of study in which high-throughput data is extracted and large amounts of advanced quantitative imaging features are analyzed from medical images, and “imaging genomics,” the field of study of high-throughput methods of associating imaging features with genomic data, has gathered academic interest. In other words, investigators have suggested that the hidden information embedded in medical images may become utilized through these Copyright © 2017 Sungkyunkwan University School of Medicine 10 Geewon Lee, et al. robust approaches. Indeed, several recent studies employing value, such as the 75th percentile CT attenuation value from ful in quantifying overall tumor spatial complexity and iden- factor for invasive adenocarcinomas [118]. Furthermore, the radiomics and imaging genomics have been found to be usetifying the tumor subregions that drive disease transformation, progression, and drug resistance [5-9]. In this review, we will look through all steps of radiomics and imaging genom- ics in oncology, specifically addressing potential applications in each organ and focusing on technical issues. Thorax Lung histograms, has been reported as a significant differentiation 97.5th percentile CT attenuation value and the slope of CT attenuation values have been suggested as predictors for fu- ture CT attenuation changes and the growth rate of pure GGO lesions [119]. Overall, lung cancer-specific (GGO-related) radiomic features could provide additional information about tumor invasiveness and progression from other indolent or non-invasive lesions and even predict tumor growth (Fig. 1). Two recent investigations support the importance of intratu- Breast [7,10]. In one study, researchers successfully divided a tumor aging genomic researches in breast imaging using MRI tex- mor subregional partitioning using multiparametric images into necrotic regions and viable regions by incorporating 18F-fluorodeoxyglucose (18F-FDG) PET and diffusion-weight ed MRI, which showed good agreement with histology [7]. In the other study, researchers identified clinically relevant, highrisk subregions in lung cancer using intratumor partitioning of 18F FDG-PET and CT images [10]. Overall, many studies have shown that textural features are associated with tumor stage, metastasis, response, survival, and metagenes in lung cancer [11-16]; thereby, providing evidence that textural features show substantial promise as prog- nostic indicators in thoracic oncology. Tables 1, 2 demonstrate the current literature about radiomics and imaging genomics in the field of clinical oncology [16-111]. In parallel with the 2011 The International Association for the Study of Lung Cancer (IASLC)/The American Thoracic Society (ATS)/The European Respiratory Society (ERS) classification for lung adenocarcinomas, an extensive volume of literature has covered the subset of subsolid nodules, which correlates with the spectrum of lung adenocarcinoma. Of particular importance is the significance of the presence and degree of a pathologically invasive portion, namely the thick- ening of alveolar septa and increased cellularity [112,113]. Although approximately half of pure ground-glass opacity (GGO) nodules have been reported to have a pathologically invasive component, discrimination between the invasive This part of the review will be focused on radiomics and imture analysis. Radiomic research has been applied to detect microcalcifications [120], differentiate benign from malignant lesions [121-123], and distinguish between breast cancer subtypes [124,125]. James et al. [120] hypothesized the magnetic susceptibility of microcalcifications leads to directional blurring effects which can be detected by statistical image processing. In their results, their method could detect localized blurring with high diagnostic performance. Regarding the differentiation between benign and malignancy, several studies have found that texture features may differ be- tween them. In the breast two-dimensional co-occurrence matrix features of dynamic contrast-enhanced (DCE) MRI images and signal enhancement ratio maps, three-dimensional and four-dimensional features may be feasible in distinguish- ing between benign and malignant breast lesions [121-123]. Holli et al. [124] have investigated to differentiate invasive (...truncated)