Radiomics of Pulmonary Nodules and Lung Cancer

Overview

Journal Transl Lung Cancer Res

Date 2017 Mar 24

PMID 28331828

Citations 104

Authors

Ryan Wilson

Anand Devaraj

Affiliations

Soon will be listed here.

Abstract

The large number of indeterminate pulmonary nodules encountered incidentally or during CT-based lung screening provides considerable diagnostic and management challenges. Conventional nodule evaluation relies on visually identifiable discriminators such as size and speculation. These visible nodule features are however small in number and subject to considerable interpretation variability. With the development of novel targeted therapies for lung cancer the diagnosis and characterization of early stage lung tumours has never been more important. Radiomics is a developing field aimed at deriving automated quantitative imaging features from medical images that can predict nodule and tumour behavior non-invasively. In contrast to conventional visual image features radiomics can extract substantially greater numbers of nodule features with much better reproducibility. This paper summarizes the basic process of radiomics and outlines why radiomic feature analysis may be particularly well suited to the evaluation of lung nodules. We review the current evidence for its clinical application with regards to pulmonary nodule management, considering promising applications such as predicting malignancy, histological subtyping, gene expression and post-treatment prognosis. Radiomics has the potential to transform the management of pulmonary nodules offering early diagnosis and personalized medicine using a method that is in cost-effective and non-invasive.

Citing Articles

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References

Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K . Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol. 2011; 22(4):796-802. DOI: 10.1007/s00330-011-2319-8. View

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

Ko J, Berman E, Kaur M, Babb J, Bomsztyk E, Greenberg A . Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry. Radiology. 2011; 262(2):662-71. PMC: 3267080. DOI: 10.1148/radiol.11100878. View

MacMahon H, Austin J, Gamsu G, Herold C, Jett J, Naidich D . Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology. 2005; 237(2):395-400. DOI: 10.1148/radiol.2372041887. View

Liu Y, Kim J, Balagurunathan Y, Li Q, Garcia A, Stringfield O . Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas. Clin Lung Cancer. 2016; 17(5):441-448.e6. PMC: 5548419. DOI: 10.1016/j.cllc.2016.02.001. View

Henschke C, Yankelevitz D, Yip R, Reeves A, Farooqi A, Xu D . Lung cancers diagnosed at annual CT screening: volume doubling times. Radiology. 2012; 263(2):578-83. PMC: 3329268. DOI: 10.1148/radiol.12102489. View

Hasegawa M, Sone S, Takashima S, Li F, Yang Z, Maruyama Y . Growth rate of small lung cancers detected on mass CT screening. Br J Radiol. 2001; 73(876):1252-9. DOI: 10.1259/bjr.73.876.11205667. View

Hawkins S, Wang H, Liu Y, Garcia A, Stringfield O, Krewer H . Predicting Malignant Nodules from Screening CT Scans. J Thorac Oncol. 2016; 11(12):2120-2128. PMC: 5545995. DOI: 10.1016/j.jtho.2016.07.002. View

Maldonado F, Boland J, Raghunath S, Aubry M, Bartholmai B, deAndrade M . Noninvasive characterization of the histopathologic features of pulmonary nodules of the lung adenocarcinoma spectrum using computer-aided nodule assessment and risk yield (CANARY)--a pilot study. J Thorac Oncol. 2013; 8(4):452-60. PMC: 3597987. DOI: 10.1097/JTO.0b013e3182843721. View

10.

Balagurunathan Y, Gu Y, Wang H, Kumar V, Grove O, Hawkins S . Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. Transl Oncol. 2014; 7(1):72-87. PMC: 3998690. DOI: 10.1593/tlo.13844. View

11.

Naidich D, Bankier A, MacMahon H, Schaefer-Prokop C, Pistolesi M, Goo J . Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology. 2012; 266(1):304-17. DOI: 10.1148/radiol.12120628. View

12.

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout R, Granton P . Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012; 48(4):441-6. PMC: 4533986. DOI: 10.1016/j.ejca.2011.11.036. View

13.

Horeweg N, van der Aalst C, Vliegenthart R, Zhao Y, Xie X, Scholten E . Volumetric computed tomography screening for lung cancer: three rounds of the NELSON trial. Eur Respir J. 2013; 42(6):1659-67. DOI: 10.1183/09031936.00197712. View

14.

Coroller T, Grossmann P, Hou Y, Velazquez E, Leijenaar R, Hermann G . CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015; 114(3):345-50. PMC: 4400248. DOI: 10.1016/j.radonc.2015.02.015. View

15.

Mattonen S, Palma D, Johnson C, Louie A, Landis M, Rodrigues G . Detection of Local Cancer Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance Versus Radiomic Assessment. Int J Radiat Oncol Biol Phys. 2016; 94(5):1121-8. DOI: 10.1016/j.ijrobp.2015.12.369. View

16.

Chae H, Park C, Park S, Lee S, Kim K, Goo J . Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology. 2014; 273(1):285-93. DOI: 10.1148/radiol.14132187. View

17.

Colen R, Foster I, Gatenby R, Giger M, Gillies R, Gutman D . NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Transl Oncol. 2014; 7(5):556-69. PMC: 4225695. DOI: 10.1016/j.tranon.2014.07.007. View

18.

Callister M, Baldwin D, Akram A, Barnard S, Cane P, Draffan J . British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax. 2015; 70 Suppl 2:ii1-ii54. DOI: 10.1136/thoraxjnl-2015-207168. View

19.

Winer-Muram H, Jennings S, Tarver R, Aisen A, Tann M, Conces D . Volumetric growth rate of stage I lung cancer prior to treatment: serial CT scanning. Radiology. 2002; 223(3):798-805. DOI: 10.1148/radiol.2233011026. View

20.

Coroller T, Agrawal V, Narayan V, Hou Y, Grossmann P, Lee S . Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol. 2016; 119(3):480-6. PMC: 4930885. DOI: 10.1016/j.radonc.2016.04.004. View