Korean J Radiol.  2020 Feb;21(2):159-171. 10.3348/kjr.2019.0630.

Radiomics in Lung Cancer from Basic to Advanced: Current Status and Future Directions

Affiliations
  • 1Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. hoyunlee96@gmail.com
  • 2Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea.
  • 3School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea.
  • 4Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.
  • 5Department of Radiology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Korea.

Abstract

Ideally, radiomics features and radiomics signatures can be used as imaging biomarkers for diagnosis, staging, prognosis, and prediction of tumor response. Thus, the number of published radiomics studies is increasing exponentially, leading to a myriad of new radiomics-based evidence for lung cancer. Consequently, it is challenging for radiologists to keep up with the development of radiomics features and their clinical applications. In this article, we review the basics to advanced radiomics in lung cancer to guide young researchers who are eager to start exploring radiomics investigations. In addition, we also include technical issues of radiomics, because knowledge of the technical aspects of radiomics supports a well-informed interpretation of the use of radiomics in lung cancer.

Keyword

Lung cancer; Biomarkers; Image processing

MeSH Terms

Biomarkers
Diagnosis
Lung Neoplasms*
Lung*
Prognosis
Biomarkers

Figure

  • Fig. 1 Steps of radiomics: radiomics process starts with image acquisition and tumor segmentation, followed by feature extraction and selection, and ends with performance testing. AUC = area under curve, KM = Kaplan-Meier

  • Fig. 2 Generation of intratumor and peritumoral regions of lung adenocarcinoma in 57-year-old woman. Yellow arrows indicate region of interests. Lower table demonstrates decrease of median Hounsfield units and increase in standard deviation from intratumor to 5 mm peritumoral and 10 mm peritumoral regions, suggesting reflection of tumor microenvironment.

  • Fig. 3 Clustering approach achieved by combining FDG-PET and contrast-enhanced CT to identify intratumor heterogeneity in 63-year-old woman with lung adenocarcinoma. Pretreatment image exhibits heterogeneous tumor areas of various colors showing multiple patterns of tumor vascularity from contrast-enhanced CT and glucose metabolism from FDG-PET. One-year posttreatment with afatinib reveals tumor with central area of low vascularity and low metabolism (blue), suggesting effective treatment. Blue area represents low vascularity and low metabolism, yellow area represents low vascularity and high metabolism or high vascularity and low metabolism, and red area represents high vascularity and high metabolism. FDG = 18F-fluorodeoxyglucose, PET = positron-emission tomography

  • Fig. 4 Interval CT images of 70-year-old man with idiopathic pulmonary fibrosis. Compared to histogram of initial CT scan, histogram of CT scan obtained three years later demonstrates right-side shifting of Hounsfield unit pixels due to microscopic interstitial fibrosis, suggesting progression of idiopathic pulmonary fibrosis.

  • Fig. 5 Example of tumor volume segmentation by two different reviewers. Results show inter-reader variability.

  • Fig. 6 Effect of CT slice thickness on tumor visualization. Thick-section CT scan (right) demonstrates more partial volume artifacts compared to thin-section CT scan (left).

  • Fig. 7 Tracking cancer evolution from primary tumor to metastases using multi-region radiomics and inter-site modeling. A. Multi-region radiomics extraction from patient with primary lung cancer in LLL and multiple metastatic nodules in LLL, LUL, and RUL. (B) Inter-site similarity matrix and (C) phylogenetic tree (based on inter-site similarity matrix) demonstrate degrees of dissimilarity with each other, which indicates evolutional sequences through all lesions. Numbers indicate numeric codes for lesion locations. LLL = left lower lobe, LUL = left upper lobe, RUL = right upper lobe

  • Fig. 8 Example of deep learning architectures for lung cancer classification based on multi-layers.


Reference

1. He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep. 2016; 6:34921.
Article
2. Dennie C, Thornhill R, Sethi-Virmani V, Souza CA, Bayanati H, Gupta A, et al. Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules. Quant Imaging Med Surg. 2016; 6:6–15.
3. Alilou M, Beig N, Orooji M, Rajiah P, Velcheti V, Rakshit S, et al. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT. Med Phys. 2017; 44:3556–3569.
Article
4. Beig N, Khorrami M, Alilou M, Prasanna P, Braman N, Orooji M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology. 2019; 290:783–792.
Article
5. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014; 5:4006.
Article
6. McGranahan N, Swanton C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell. 2015; 27:15–26.
Article
7. 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. 2012; 22:796–802.
Article
8. Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology. 2013; 266:326–336.
Article
9. Win T, Miles KA, Janes SM, Ganeshan B, Shastry M, Endozo R, et al. Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res. 2013; 19:3591–3599.
Article
10. Fried DV, Tucker SL, Zhou S, Liao Z, Mawlawi O, Ibbott G, et al. Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys. 2014; 90:834–842.
11. Cherezov D, Goldgof D, Hall L, Gillies R, Schabath M, Müller H, et al. Revealing tumor habitats from texture heterogeneity analysis for classification of lung cancer malignancy and aggressiveness. Sci Rep. 2019; 9:4500.
Article
12. Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013; 54:19–26.
Article
13. Cook GJ, O'Brien ME, Siddique M, Chicklore S, Loi HY, Sharma B, et al. Non-small cell lung cancer treated with erlotinib: heterogeneity of 18F-FDG uptake at PET—Association with treatment response and prognosis. Radiology. 2015; 276:883–893.
14. Papageorgiou CV, Antoniou D, Kaltsakas G, Koulouris NG. Role of quantitative CT in predicting postoperative FEV1 and chronic dyspnea in patients undergoing lung resection. Multidiscip Respir Med. 2010; 5:188–193.
Article
15. Poonyagariyagorn H, Mazzone PJ. Lung cancer: preoperative pulmonary evaluation of the lung resection candidate. Semin Respir Crit Care Med. 2008; 29:271–284.
Article
16. Wu MT, Chang JM, Chiang AA, Lu JY, Hsu HK, Hsu WH, et al. Use of quantitative CT to predict postoperative lung function in patients with lung cancer. Radiology. 1994; 191:257–262.
Article
17. 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.
18. Maldonado F, Moua T, Rajagopalan S, Karwoski RA, Raghunath S, Decker PA, et al. Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis. Eur Respir J. 2014; 43:204–212.
Article
19. 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; 26:1368–1377.
Article
20. 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; 207:976–983.
Article
21. Yoon RG, Seo JB, Kim N, Lee HJ, Lee SM, Lee YK, et al. Quantitative assessment of change in regional disease patterns on serial HRCT of fibrotic interstitial pneumonia with texture-based automated quantification system. Eur Radiol. 2013; 23:692–701.
Article
22. Yang X, Pan X, Liu H, Gao D, He J, Liang W, et al. A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram. J Thorac Dis. 2018; 10:Suppl 7. S807–S819.
Article
23. Zhong Y, Yuan M, Zhang T, Zhang YD, Li H, Yu TF. Radiomics approach to prediction of occult mediastinal lymph node metastasis of lung adenocarcinoma. AJR Am J Roentgenol. 2018; 211:109–113.
Article
24. Bayanati H, Thornhill RE, Souza CA, Sethi-Virmani V, Gupta A, Maziak D, et al. Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer? Eur Radiol. 2015; 25:480–487.
Article
25. Andersen MB, Harders SW, Ganeshan B, Thygesen J, Torp Madsen HH, Rasmussen F. CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer. Acta Radiol. 2016; 57:669–676.
Article
26. Coroller TP, Agrawal V, Huynh E, Narayan V, Lee SW, Mak RH, et al. Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol. 2017; 12:467–476.
Article
27. Li H, Becker N, Raman S, Chan TC, Bissonnette JP. The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT. Med Phys. 2015; 42:4727–4733.
Article
28. Gevaert O, Echegaray S, Khuong A, Hoang CD, Shrager JB, Jensen KC, et al. Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci Rep. 2017; 7:41674.
Article
29. Rizzo S, Petrella F, Buscarino V, De Maria F, Raimondi S, Barberis M, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol. 2016; 26:32–42.
Article
30. Yoon HJ, Sohn I, Cho JH, Lee HY, Kim JH, Choi YL, et al. Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach. Medicine (Baltimore). 2015; 94:e1753.
Article
31. Zhou M, Leung A, Echegaray S, Gentles A, Shrager JB, Jensen KC, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology. 2018; 286:307–315.
Article
32. Nair VS, Gevaert O, Davidzon G, Napel S, Graves EE, Hoang CD, et al. Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res. 2012; 72:3725–3734.
Article
33. Padhani AR, Miles KA. Multiparametric imaging of tumor response to therapy. Radiology. 2010; 256:348–364.
Article
34. Lee HY, Jeong JY, Lee KS, Yi CA, Kim BT, Kang H, et al. Histopathology of lung adenocarcinoma based on new IASLC/ATS/ERS classification: prognostic stratification with functional and metabolic imaging biomarkers. J Magn Reson Imaging. 2013; 38:905–913.
Article
35. Kim J, Ryu SY, Lee SH, Lee HY, Park H. Clustering approach to identify intratumour heterogeneity combining FDG PET and diffusion-weighted MRI in lung adenocarcinoma. Eur Radiol. 2019; 29:468–475.
Article
36. Even AJG, Reymen B, La Fontaine MD, Das M, Mottaghy FM, Belderbos JSA, et al. Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer. Radiother Oncol. 2017; 125:379–384.
Article
37. Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One. 2014; 9:e102107.
Article
38. Lassen BC, Jacobs C, Kuhnigk JM, van Ginneken B, van Rikxoort EM. Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans. Phys Med Biol. 2015; 60:1307–1323.
Article
39. Ashraf H, de Hoop B, Shaker SB, Dirksen A, Bach KS, Hansen H, et al. Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably. Eur Radiol. 2010; 20:1878–1885.
Article
40. Devaraj A, van Ginneken B, Nair A, Baldwin D. Use of volumetry for lung nodule management: theory and practice. Radiology. 2017; 284:630–644.
Article
41. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017; 35:18–31.
Article
42. Trebeschi S, van Griethuysen JJM, Lambregts DMJ, Lahaye MJ, Parmar C, Bakers FCH, et al. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci Rep. 2017; 7:5301.
Article
43. Zhao B, Tan Y, Tsai WY, Schwartz LH, Lu L. Exploring variability in CT characterization of tumors: a preliminary phantom study. Transl Oncol. 2014; 7:88–93.
Article
44. Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016; 6:23428.
Article
45. Yip S, McCall K, Aristophanous M, Chen AB, Aerts HJ, Berbeco R. Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer. PLoS One. 2014; 9:e115510.
Article
46. Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG. Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol. 2015; 8:524–534.
Article
47. Du Q, Baine M, Bavitz K, McAllister J, Liang X, Yu H, et al. Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction. PLoS One. 2019; 14:e0216480.
Article
48. Kim H, Park CM, Lee M, Park SJ, Song YS, Lee JH, et al. Impact of reconstruction algorithms on CT radiomic features of pulmonary tumors: analysis of intra- and inter-reader variability and inter-reconstruction algorithm variability. PLoS One. 2016; 11:e0164924.
Article
49. Lo P, Young S, Kim HJ, Brown MS, McNitt-Gray MF. Variability in CT lung-nodule quantification: effects of dose reduction and reconstruction methods on density and texture based features. Med Phys. 2016; 43:4854.
Article
50. Solomon J, Mileto A, Nelson RC, Roy Choudhury K, Samei E. Quantitative features of liver lesions, lung nodules, and renal stones at multi-detector row CT examinations: dependency on radiation dose and reconstruction algorithm. Radiology. 2016; 279:185–194.
Article
51. Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D, et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 2017; 7:588.
Article
52. Alahmari SS, Cherezov D, Goldgof D, Hall L, Gillies RJ, Schabath MB. Delta radiomics improves pulmonary nodule malignancy prediction in lung cancer screening. IEEE Access. 2018; 6:77796–77806.
Article
53. Vargas HA, Veeraraghavan H, Micco M, Nougaret S, Lakhman Y, Meier AA, et al. A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. Eur Radiol. 2017; 27:3991–4001.
Article
54. van der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res. 2014; 15:3221–3245.
55. Prasanna P, Tiwari P, Madabhushi A. Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): a new radiomics descriptor. Sci Rep. 2016; 6:37241.
Article
56. Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi AM, Mirabelli P, et al. Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci. 2017; 18:E805.
Article
57. Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuzé S, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017; 28:1191–1206.
Article
58. Kim ST, Lee JH, Lee H, Ro YM. Visually interpretable deep network for diagnosis of breast masses on mammograms. Phys Med Biol. 2018; 63:235025.
Article
59. Tang Z, Chuang KV, DeCarli C, Jin LW, Beckett L, Keiser MJ, et al. Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. Nat Commun. 2019; 10:2173.
Article
60. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging. 2016; 35:1299–1312.
Article
61. Patz EF Jr, Caporaso NE, Dubinett SM, Massion PP, Hirsch FR, Minna JD, et al. National Lung Cancer Screening Trial American College of Radiology Imaging Network specimen biorepository originating from the contemporary screening for the detection of lung cancer trial (NLST, ACRIN 6654): design, intent, and availability of specimens for validation of lung cancer biomarkers. J Thorac Oncol. 2010; 5:1502–1506.
Article
62. Ru Zhao Y, Xie X, de Koning HJ, Mali WP, Vliegenthart R, Oudkerk M. NELSON lung cancer screening study. Cancer Imaging. 2011; 11 Spec No A:S79–S84.
Article
63. Sanchez-Salcedo P, Berto J, de-Torres JP, Campo A, Alcaide AB, Bastarrika G, et al. Lung cancer screening: fourteen year experience of the Pamplona early detection program (P-IELCAP). Arch Bronconeumol. 2015; 51:169–176.
Article
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