Acknowledgement
We are thankful to Seung-Hak Lee, and Jonghoon Kim, PhD, from Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea, who devoted their time and knowledge in technical support to provide figures for this article.
References
- 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 https://doi.org/10.1038/srep34921
- 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
- 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 https://doi.org/10.1002/mp.12208
- 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 https://doi.org/10.1148/radiol.2018180910
- 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 https://doi.org/10.1038/ncomms5006
- McGranahan N, Swanton C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 2015;27:15-26 https://doi.org/10.1016/j.ccell.2014.12.001
- 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 https://doi.org/10.1007/s00330-011-2319-8
- 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 https://doi.org/10.1148/radiol.12112428
- 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 https://doi.org/10.1158/1078-0432.CCR-12-1307
- 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 https://doi.org/10.1016/j.ijrobp.2014.07.020
- Cherezov D, Goldgof D, Hall L, Gillies R, Schabath M, Muller H, et al. Revealing tumor habitats from texture heterogeneity analysis for classification of lung cancer malignancy and aggressiveness. Sci Rep 2019;9:4500 https://doi.org/10.1038/s41598-019-38831-0
- 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 https://doi.org/10.2967/jnumed.112.107375
- 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 https://doi.org/10.1148/radiol.2015141309
- 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 https://doi.org/10.1186/2049-6958-5-3-188
- Poonyagariyagorn H, Mazzone PJ. Lung cancer: preoperative pulmonary evaluation of the lung resection candidate. Semin Respir Crit Care Med 2008;29:271-284 https://doi.org/10.1055/s-2008-1076747
- 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 https://doi.org/10.1148/radiology.191.1.8134584
- 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 https://doi.org/10.1148/radiol.2017161177
- 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 https://doi.org/10.1183/09031936.00071812
- 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 https://doi.org/10.1007/s00330-015-3946-2
- 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 https://doi.org/10.2214/AJR.16.16054
- 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
- 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 https://doi.org/10.21037/jtd.2018.03.126
- 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 https://doi.org/10.2214/AJR.17.19074
- 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 https://doi.org/10.1007/s00330-014-3420-6
- 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 https://doi.org/10.1177/0284185115598808
- 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 https://doi.org/10.1016/j.jtho.2016.11.2226
- 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 https://doi.org/10.1118/1.4926755
- 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 https://doi.org/10.1038/srep41674
- 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 https://doi.org/10.1007/s00330-015-3814-0
- 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 https://doi.org/10.1097/MD.0000000000001753
- 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 https://doi.org/10.1148/radiol.2017161845
- 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 https://doi.org/10.1158/0008-5472.CAN-11-3943
- Padhani AR, Miles KA. Multiparametric imaging of tumor response to therapy. Radiology 2010;256:348-364 https://doi.org/10.1148/radiol.10091760
- 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 https://doi.org/10.1002/jmri.24080
- 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 https://doi.org/10.1007/s00330-018-5590-0
- 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 https://doi.org/10.1016/j.radonc.2017.09.041
- 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 https://doi.org/10.1371/journal.pone.0102107
- 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 https://doi.org/10.1088/0031-9155/60/3/1307
- 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 https://doi.org/10.1007/s00330-010-1749-z
- Devaraj A, van Ginneken B, Nair A, Baldwin D. Use of volumetry for lung nodule management: theory and practice. Radiology 2017;284:630-644 https://doi.org/10.1148/radiol.2017151022
- 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 https://doi.org/10.1016/j.media.2016.05.004
- Trebeschi S, van Griethuysen JJM, Lambregts DMJ, Lahaye MJ, Parmar C, Bakers FCH, et al. Deep learning for fullyautomated localization and segmentation of rectal cancer on multiparametric MR. Sci Rep 2017;7:5301 https://doi.org/10.1038/s41598-017-05728-9
- 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 https://doi.org/10.1593/tlo.13865
- 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 https://doi.org/10.1038/srep23428
- 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 https://doi.org/10.1371/journal.pone.0115510
- 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 https://doi.org/10.1016/j.tranon.2015.11.013
- 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 https://doi.org/10.1371/journal.pone.0216480
- 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 https://doi.org/10.1371/journal.pone.0164924
- 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 https://doi.org/10.1118/1.4954845
- 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 https://doi.org/10.1148/radiol.2015150892
- 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 https://doi.org/10.1038/s41598-017-00665-z
- 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 https://doi.org/10.1109/ACCESS.2018.2884126
- 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 https://doi.org/10.1007/s00330-017-4779-y
- van der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res 2014;15:3221-3245
- Prasanna P, Tiwari P, Madabhushi A. Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): a new radiomics descriptor. Sci Rep 2016;6:37241 https://doi.org/10.1038/srep37241
- 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. pii: E805
- Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuze S, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 2017;28:1191-1206 https://doi.org/10.1093/annonc/mdx034
- 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 https://doi.org/10.1088/1361-6560/aaef0a
- 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 https://doi.org/10.1038/s41467-019-10212-1
- 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 https://doi.org/10.1109/TMI.2016.2535302
- 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 https://doi.org/10.1097/JTO.0b013e3181f1c634
- 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 https://doi.org/10.1102/1470-7330.2011.9020
- 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
Cited by
- Beyond DNA-targeting in Cancer Chemotherapy. Emerging Frontiers - A Review vol.20, 2020, https://doi.org/10.2174/1568026620666200819160213
- A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy vol.104, pp.1, 2020, https://doi.org/10.1177/0036850421997295