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.
- 1.The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012;489:519–25.Google Scholar
- 2.The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543–50.Google Scholar
- 7.Son JY, Lee HY, Kim JH, Han J, Jeong JY, Lee KS, et al. Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non-invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping. Eur Radiol. 2016;26:43–54.CrossRefPubMedGoogle Scholar
- 31.Wu J, Gensheimer MF, Dong X, Rubin DL, Napel S, Diehn M, et al. Robust intratumor partitioning to identify high-risk subregions in lung cancer: a pilot study. Int J Radiat Oncol Biol Phys. 2016;95:1504–12.Google Scholar
- 35.Choi S, Hoffman EA, Wenzel SE, Castro M, Fain S, Jarjour N, et al. Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes. J Allergy Clin Immunol. 2017;140:690-700.e8.Google Scholar
- 45.Yabuuchi H, Kawanami S, Kamitani T, Yonezawa M, Yamasaki Y, Yamanouchi T, et al. Prediction of post-operative pulmonary function after lobectomy for primary lung cancer: a comparison among counting method, effective lobar volume, and lobar collapsibility using inspiratory/expiratory CT. Eur J Radiol. 2016;85:1956–62.CrossRefPubMedGoogle Scholar
- 53.Lapointe A, Bahig H, Blais D, Bouchard H, Filion E, Carrier JF, et al. Assessing lung function using contrast-enhanced dual energy computed tomography for potential applications in radiation therapy. Med Phys. 2017. https://doi.org/10.1002/mp.12475.
- 59.Humphries SM, Yagihashi K, Huckleberry J, Rho BH, Schroeder JD, Strand M, et al. Idiopathic pulmonary fibrosis: data-driven textural analysis of extent of fibrosis at baseline and 15-month follow-up. Radiology. 2017;285:270-278. Google Scholar
- 61.Moon JW, Bae JP, Lee HY, Kim N, Chung MP, Park HY, et al. Perfusion- and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed tomography in diffuse interstitial lung disease: relationships with physiologic impairment and prediction of prognosis. Eur Radiol. 2016;26:1368–77.CrossRefPubMedGoogle Scholar
- 62.Park HJ, Lee SM, Song JW, Lee SM, Oh SY, Kim N, et al. Texture-based automated quantitative assessment of regional patterns on initial CT in patients with idiopathic pulmonary fibrosis: relationship to decline in forced vital capacity. AJR Am J Roentgenol. 2016;207:976–83.CrossRefPubMedGoogle Scholar