EGFR Mutation Status and Subtypes Predicted by CT-Based 3D Radiomic Features in Lung Adenocarcinoma

References

• Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–249. doi:10.3322/caac.21660
   PubMed Web of Science ®Google Scholar
• Dong S, Men W, Yang S, Xu S. Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples. Oncol Rep. 2020;43:1437–1450. doi:10.3892/or.2020.7526
   PubMed Web of Science ®Google Scholar
• Travis WD, Brambilla E, Nicholson AG, et al. The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol. 2015;10:1243–1260. doi:10.1097/JTO.0000000000000630
   PubMed Web of Science ®Google Scholar
• Balagurunathan Y, Gu Y, Wang H, et al. Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol. 2014;7:72–87. doi:10.1593/tlo.13844
   PubMed Web of Science ®Google Scholar
• Panth KM, Leijenaar RT, Carvalho S, et al. Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells. Radiother Oncol. 2015;116:462–466. doi:10.1016/j.radonc.2015.06.013
   PubMed Web of Science ®Google Scholar
• Stewart EL, Tan SZ, Liu G, Tsao MS. Known and putative mechanisms of resistance to EGFR targeted therapies in NSCLC patients with EGFR mutations-a review. Transl Lung Cancer Res. 2015;4:67–81. doi:10.3978/j.issn.2218-6751.2014.11.06
   PubMed Web of Science ®Google Scholar
• Coroller TP, Grossmann P, Hou Y, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015;114:345–350. doi:10.1016/j.radonc.2015.02.015
   PubMed Web of Science ®Google Scholar
• Fried DV, Tucker SL, Zhou S, 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. doi:10.1016/j.ijrobp.2014.07.020
   PubMed Web of Science ®Google Scholar
• Parmar C, Rios Velazquez E, Leijenaar R, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One. 2014;9:e102107. doi:10.1371/journal.pone.0102107
   PubMed Web of Science ®Google Scholar
• Hendriks LE, Derks JL, Postmus PE, et al. Single organ metastatic disease and local disease status, prognostic factors for overall survival in stage IV non-small cell lung cancer: results from a population-based study. Eur J Cancer. 2015;51:2534–2544. doi:10.1016/j.ejca.2015.08.008
   PubMed Web of Science ®Google Scholar
• Rizzo S, Petrella F, Buscarino V, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol. 2016;26:32–42. doi:10.1007/s00330-015-3814-0
   PubMed Web of Science ®Google Scholar
• Yoon HY, Ryu JS, Sim YS, et al. Clinical significance of EGFR mutation types in lung adenocarcinoma: a multi-centre Korean study. PLoS One. 2020;15:e0228925. doi:10.1371/journal.pone.0228925
   PubMed Web of Science ®Google Scholar
• Choi YW, Jeon SY, Jeong GS, et al. EGFR Exon 19 deletion is associated with favorable overall survival after first-line gefitinib therapy in advanced non-small cell lung cancer patients. Am J Clin Oncol. 2018;41:385–390. doi:10.1097/COC.0000000000000282
   PubMed Web of Science ®Google Scholar
• Rossi S, Toschi L, Finocchiaro G, et al. Impact of Exon 19 deletion subtypes in EGFR-mutant metastatic non-small-cell lung cancer treated with first-line tyrosine kinase inhibitors. Clin Lung Cancer. 2019;20:82–87. doi:10.1016/j.cllc.2018.10.009
   PubMed Web of Science ®Google Scholar
• Wu W, Parmar C, Grossmann P, et al. Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol. 2016;6:71. doi:10.3389/fonc.2016.00071
   PubMed Web of Science ®Google Scholar
• Mackin D, Fave X, Zhang L, et al. Harmonizing the pixel size in retrospective computed tomography radiomics studies. PLoS One. 2017;12:e0178524. doi:10.1371/journal.pone.0178524
   PubMed Web of Science ®Google Scholar
• Antropova N, Huynh BQ, Giger ML. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys. 2017;44:5162–5171. doi:10.1002/mp.12453
   PubMed Web of Science ®Google Scholar
• Wong CW, Chaudhry A. Radiogenomics of lung cancer. J Thorac Dis. 2020;12:5104–5109. doi:10.21037/jtd-2019-pitd-10
   PubMed Web of Science ®Google Scholar
• Lee HJ, Kim YT, Kang CH, et al. Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes. Radiology. 2013;268:254–264. doi:10.1148/radiol.13112553
   PubMed Web of Science ®Google Scholar
• Gu Y, Kumar V, Hall LO, et al. Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recognit. 2013;46:692–702. doi:10.1016/j.patcog.2012.10.005
   PubMed Web of Science ®Google Scholar
• Dingemans AM, Groen HJ, Herder GJ, et al. A randomized Phase II study comparing paclitaxel-carboplatin-bevacizumab with or without nitroglycerin patches in patients with stage IV nonsquamous nonsmall-cell lung cancer: NVALT12 (NCT01171170) dagger. Ann Oncol. 2015;26:2286–2293. doi:10.1093/annonc/mdv370
   PubMed Web of Science ®Google Scholar
• Larue R, van Timmeren JE, de Jong EEC, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol. 2017;56:1544–1553. doi:10.1080/0284186X.2017.1351624
   PubMed Web of Science ®Google Scholar
• Shafiq-Ul-Hassan M, Zhang GG, Latifi K, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44:1050–1062. doi:10.1002/mp.12123
   PubMed Web of Science ®Google Scholar
• Aerts HJ, Grossmann P, Tan Y, et al. Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep. 2016;6:33860. doi:10.1038/srep33860
   PubMed Web of Science ®Google Scholar
• Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F. Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep. 2017;7:46349. doi:10.1038/srep46349
   PubMed Web of Science ®Google Scholar
• Zhou JY, Zheng J, Yu ZF, et al. Comparative analysis of clinicoradiologic characteristics of lung adenocarcinomas with ALK rearrangements or EGFR mutations. Eur Radiol. 2015;25:1257–1266. doi:10.1007/s00330-014-3516-z
   PubMed Web of Science ®Google Scholar
• Eberhardt WE, Mitchell A, Crowley J, et al. The IASLC lung cancer staging project: proposals for the revision of the M descriptors in the forthcoming eighth edition of the TNM classification of lung cancer. J Thorac Oncol. 2015;10:1515–1522. doi:10.1097/JTO.0000000000000673
   PubMed Web of Science ®Google Scholar
• Goldstraw P, Chansky K, Crowley J, et al. The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol. 2016;11:39–51. doi:10.1016/j.jtho.2015.09.009
   PubMed Web of Science ®Google Scholar
• Yuan R, Shi S, Chen J, Cheng G. Radiomics in rayplus: a web-based tool for texture analysis in medical images. J Digit Imaging. 2019;32:269–275. doi:10.1007/s10278-018-0128-1
   PubMed Web of Science ®Google Scholar
• Yip SS, Kim J, Coroller TP, et al. Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer. J Nucl Med. 2017;58:569–576. doi:10.2967/jnumed.116.181826
   PubMed Web of Science ®Google Scholar
• Liu Y, Kim J, Balagurunathan Y, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer. 2016;17:441–448 e446. doi:10.1016/j.cllc.2016.02.001
   PubMed Web of Science ®Google Scholar
• Liu G, Xu Z, Ge Y, et al. 3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma. Transl Lung Cancer Res. 2020;9:1212–1224. doi:10.21037/tlcr-20-122
   PubMed Web of Science ®Google Scholar
• Fukuoka M, Wu YL, Thongprasert S, et al. Biomarker analyses and final overall survival results from a Phase III, randomized, open-label, first-line study of gefitinib versus carboplatin/paclitaxel in clinically selected patients with advanced non-small-cell lung cancer in Asia (IPASS). J Clin Oncol. 2011;29:2866–2874. doi:10.1200/JCO.2010.33.4235
   PubMed Web of Science ®Google Scholar
• Zhou C, Wu YL, Chen G, et al. Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multicentre, open-label, randomised, Phase 3 study. Lancet Oncol. 2011;12:735–742. doi:10.1016/S1470-2045(11)70184-X
   PubMed Web of Science ®Google Scholar
• Tseng CH, Chen KC, Hsu KH, et al. EGFR mutation and lobar location of lung adenocarcinoma. Carcinogenesis. 2016;37:157–162. doi:10.1093/carcin/bgv168
   PubMed Web of Science ®Google Scholar
• Tu W, Sun G, Fan L, et al. Radiomics signature: a potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology. Lung Cancer. 2019;132:28–35. doi:10.1016/j.lungcan.2019.03.025
   PubMed Web of Science ®Google Scholar