Radiogenomics in non-small cell lung cancer

Abstract

Abstract:

Radiogenomics explores the relationship between imaging features and gene expression patterns using radiomics, which is non-invasive and can present the overall information of tumors. The application of radiomics, somewhat effective in predicting gene mutations in non-small cell lung cancer (NSCLC), has recently become a research focus. Radiomic features, combined with conventional imaging, clinical and other features, can provide multi-directional information on tumors and play an increasingly important role in the prediction and precise treatment of NSCLC-driving gene phenotypes.

Key words:Carcinoma,non-small-cell lung,Receptor,epidermal growth factor,Anaplastic lymphoma kinase,KRAS gene,Radiomics

Yang Lei, Zhang Chuanyu, Zhang Zaixian, Liu Huan. Radiogenomics in non-small cell lung cancer[J]. Journal of International Oncology, 2020, 47(9): 555-559.

References35

[1]	Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020[J]. CA Cancer J Clin, 2020,70(1):7-30. DOI: 10.3322/caac.21590. doi:10.3322/caac.21590pmid:31912902
[2]	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017,14(12):749-762. DOI: 10.1038/nrclinonc.2017.141. pmid:28975929
[3]	Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications[J]. Radiology, 2018,286(1):307-315. DOI: 10.1148/radiol.2017161845. doi:10.1148/radiol.2017161845pmid:28727543
[4]	Liu Y, Kim J, Balagurunathan Y, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas[J]. Clin Lung Cancer, 2016,17(5):441-448.e6. DOI: 10.1016/j.cllc.2016.02.001. doi:10.1016/j.cllc.2016.02.001pmid:27017476
[5]	Song J, Shi J, Dong D, et al. A new approach to predict progression-free survival in stage Ⅳ EGFR-mutant NSCLC patients with EGFR-TKI therapy[J]. Clin Cancer Res, 2018,24(15):3583-3592. DOI: 10.1158/1078-0432.CCR-17-2507. doi:10.1158/1078-0432.CCR-17-2507pmid:29563137
[6]	Zhang L, Chen B, Liu X, et al. Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer[J]. Transl Oncol, 2018,11(1):94-101. DOI: 10.1016/j.tranon.2017.10.012. pmid:29216508
[7]	Jia TY, Xiong JF, Li XY, et al. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling[J]. Eur Radiol, 2019,29(9):4742-4750. DOI: 10.1007/s00330-019-06024-y. doi:10.1007/s00330-019-06024-ypmid:30778717
[8]	Yang X, Dong X, Wang J, et al. Computed tomography-based radiomics signature: a potential indicator of epidermal growth factor receptor mutation in pulmonary adenocarcinoma appearing as a subsolid nodule[J]. Oncologist, 2019,24(11):e1156-e1164. DOI: 10.1634/theoncologist.2018-0706. doi:10.1634/theoncologist.2018-0706pmid:30936378
[9]	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[J]. Lung Cancer, 2019,132:28-35. DOI: 10.1016/j.lungcan.2019.03.025. doi:10.1016/j.lungcan.2019.03.025
[10]	Wang S, Shi J, Ye Z, et al. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning[J]. Eur Respir J, 2019,53(3):1800986. DOI: 10.1183/13993003.00986-2018. doi:10.1183/13993003.00986-2018pmid:30635290
[11]	Li S, Ding C, Zhang H, et al. Radiomics for the prediction of EGFR mutation subtypes in non-small cell lung cancer[J]. Med Phys, 2019,46(10):4545-4552. DOI: 10.1002/mp.13747. doi:10.1002/mp.13747pmid:31376283
[12]	Zhao W, Wu Y, Xu Y, et al. The potential of radiomics nomogram in non-invasively prediction of epidermal growth factor receptor mutation status and subtypes in lung adenocarcinoma[J]. Front Oncol, 2020,9:1485. DOI: 10.3389/fonc.2019.01485. doi:10.3389/fonc.2019.01485pmid:31993370
[13]	Wang H, Guo H, Wang Z, et al. The diagnostic value of quantitative CT analysis of ground-glass volume percentage in differentiating epidermal growth factor receptor mutation and subtypes in lung adenocarcinoma[J]. Biomed Res Int, 2019,2019:9643836. DOI: 10.1155/2019/9643836. doi:10.1155/2019/9643836pmid:30956990
[14]	Suh YJ, Lee HJ, Kim YJ, et al. Computed tomography characteristics of lung adenocarcinomas with epidermal growth factor receptor mutation: a propensity score matching study[J]. Lung Cancer, 2018,123:52-59. DOI: 10.1016/j.lungcan.2018.06.030. doi:10.1016/j.lungcan.2018.06.030pmid:30089595
[15]	Hong SJ, Kim TJ, Choi YW, et al. Radiogenomic correlation in lung adenocarcinoma with epidermal growth factor receptor mutations: imaging features and histological subtypes[J]. Eur Radiol, 2016,26(10):3660-3668. DOI: 10.1007/s00330-015-4196-z. doi:10.1007/s00330-015-4196-zpmid:26787602
[16]	Hasegawa M, Sakai F, Ishikawa R, et al. CT features of epidermal growth factor receptor-mutated adenocarcinoma of the lung: comparison with nonmutated adenocarcinoma[J]. J Thorac Oncol, 2016,11(6):819-826. DOI: 10.1016/j.jtho.2016.02.010. doi:10.1016/j.jtho.2016.02.010
[17]	Liu Y, Kim J, Qu F, et al. CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma[J]. Radiology, 2016,280(1):271-280. DOI: 10.1148/radiol.2016151455. doi:10.1148/radiol.2016151455pmid:26937803
[18]	Qin X, Gu X, Lu Y, et al. EGFR-TKI-sensitive mutations in lung carcinomas: are they related to clinical features and CT findings?[J]. Cancer Manag Res, 2018,10:4019-4027. DOI: 10.2147/CMAR.S174623. doi:10.2147/CMAR.S174623pmid:30323660
[19]	Koo HJ, Kim MY, Park S, et al. Non-small cell lung cancer with resistance to EGFR-TKI therapy: CT characteristics of T790M mutation-positive cancer[J]. Radiology, 2018,289(1):227-237. DOI: 10.1148/radiol.2018180070. doi:10.1148/radiol.2018180070pmid:30015588
[20]	Yoon HJ, Sohn I, Cho JH, et al. Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach[J]. Medicine (Baltimore), 2015,94(41):e1753. DOI: 10.1097/MD.0000000000001753. doi:10.1097/MD.0000000000001753
[21]	宋兰, 朱振宸, 姜蕾, 等. CT影像组学在预测肺腺癌ALK融合基因表达中的价值初探[J]. 中华放射学杂志, 2019,53(11):963-967. DOI: 10.3760/cma.j.issn.1005-1201.2019.11.007.
[22]	Song L, Zhu Z, Mao L, et al. Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients[J]. Front Oncol, 2020,10:369. DOI: 10.3389/fonc.2020.00369. doi:10.3389/fonc.2020.00369pmid:32266148
[23]	Kim TJ, Lee CT, Jheon SH, et al. Radiologic characteristics of surgically resected non-small cell lung cancer with ALK rearrangement or EGFR mutations[J]. Ann Thorac Surg, 2016,101(2):473-480. DOI: 10.1016/j.athoracsur.2015.07.062. doi:10.1016/j.athoracsur.2015.07.062pmid:26454747
[24]	Wang H, Schabath MB, Liu Y, et al. Clinical and CT characteristics of surgically resected lung adenocarcinomas harboring ALK rearrangements or EGFR mutations[J]. Eur J Radiol, 2016,85(11):1934-1940. DOI: 10.1016/j.ejrad.2016.08.023. doi:10.1016/j.ejrad.2016.08.023pmid:27776643
[25]	Mori M, Hayashi H, Fukuda M, et al. Clinical and computed tomography characteristics of non-small cell lung cancer with ALK gene rearrangement: comparison with EGFR mutation and ALK/EGFR-negative lung cancer[J]. Thorac Cancer, 2019,10(4):872-879. DOI: 10.1111/1759-7714.13017. doi:10.1111/tca.2019.10.issue-4
[26]	Rizzo S, Petrella F, Buscarino V, et al. CT radiogenomic characte-rization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer[J]. Eur Radiol, 2016,26(1):32-42. DOI: 10.1007/s00330-015-3814-0. doi:10.1007/s00330-015-3814-0pmid:25956936
[27]	Miao Y, Zhu S, Li H, et al. Comparison of clinical and radiological characteristics between anaplastic lymphoma kinase rearrangement and epidermal growth factor receptor mutation in treatment naïve advanced lung adenocarcinoma[J]. J Thorac Dis, 2017,9(10):3927-3937. DOI: 10.21037/jtd.2017.08.134. doi:10.21037/jtd.2017.08.134pmid:29268403
[28]	Mendoza DP, Stowell J, Muzikansky A, et al. Computed tomography imaging characteristics of non-small-cell lung cancer with anaplastic lymphoma kinase rearrangements: a systematic review and meta-analysis[J]. Clin Lung Cancer, 2019,20(5):339-349. DOI: 10.1016/j.cllc.2019.05.006. doi:10.1016/j.cllc.2019.05.006
[29]	Seto K, Kuroda H, Yoshida T, et al. Higher frequency of occult lymph node metastasis in clinical N₀pulmonary adenocarcinoma with ALK rearrangement[J]. Cancer Manag Res, 2018,10:2117-2124. DOI: 10.2147/CMAR.S147569. doi:10.2147/CMAR.S147569pmid:30050322
[30]	Weiss GJ, Ganeshan B, Miles KA, et al. Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic[J]. PLoS One, 2014,9(7):e100244. DOI: 10.1371/journal.pone.0100244. doi:10.1371/journal.pone.0100244pmid:24987838
[31]	Rios Velazquez E, Parmar C, Liu Y, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer[J]. Cancer Res, 2017,77(14):3922-3930. DOI: 10.1158/0008-5472.CAN-17-0122. doi:10.1158/0008-5472.CAN-17-0122pmid:28566328
[32]	Wang H, Schabath MB, Liu Y, et al. Association between computed tomographic features and kirsten rat sarcoma viral oncogene mutations in patients with stage Ⅰ lung adenocarcinoma and their prognostic value[J]. Clin Lung Cancer, 2016,17(4):271-278. DOI: 10.1016/j.cllc.2015.11.002. doi:10.1016/j.cllc.2015.11.002pmid:26712103
[33]	Lv J, Zhang H, Ma J, et al. Comparison of CT radiogenomic and clinical characteristics between EGFR and KRAS mutations in lung adenocarcinomas[J]. Clin Radiol, 2018,73(6):590.e1-590.e8. DOI: 10.1016/j.crad.2018.01.009. doi:10.1016/j.crad.2018.01.009
[34]	Rizzo S, Raimondi S, de Jong EEC, et al. Genomics of non-small cell lung cancer (NSCLC): association between CT-based imaging features and EGFR and K-RAS mutations in 122 patients—an external validation[J]. Eur J Radiol, 2019,110:148-155. DOI: 10.1016/j.ejrad.2018.11.032. doi:10.1016/j.ejrad.2018.11.032pmid:30599853
[35]	Gevaert O, Echegaray S, Khuong A, et al. Predictive radiogenomics modeling of EGFR mutation status in lung cancer[J]. Sci Rep, 2017,7:41674. DOI: 10.1038/srep41674. pmid:28139704