非小细胞肺癌影像基因组学

doi:10.3760/cma.j.cn371439-20200423-00077

摘要/Abstract

摘要：

影像基因组学用影像组学方法探索影像学特征与基因表达模式之间的联系,具有无创、能显示肿瘤整体信息的特点。影像组学的应用对预测非小细胞肺癌(NSCLC)的基因突变具有一定作用,是近年来研究的热点。影像组学特征与常规影像学特征、临床特征等联系起来,可提供肿瘤的多方位信息,在NSCLC驱动基因表型的预测和精准治疗中将发挥越来越重要的作用。

关键词: 癌,非小细胞肺, 受体,表皮生长因子, 间变性淋巴瘤激酶, KRAS基因, 影像组学

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

杨蕾, 张传玉, 张在先, 刘欢. 非小细胞肺癌影像基因组学[J]. 国际肿瘤学杂志, 2020, 47(9): 555-559.

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.

参考文献 35

[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.21590 pmid: 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.2017161845 pmid: 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.2016.02.001 pmid: 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-17-2507 pmid: 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-y pmid: 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.2018-0706 pmid: 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: 10.1183/13993003.00986-2018 pmid: 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.13747 pmid: 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.2019.01485 pmid: 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: 10.1155/2019/9643836 pmid: 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.2018.06.030 pmid: 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-z pmid: 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.2016151455 pmid: 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.S174623 pmid: 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.2018180070 pmid: 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.2020.00369 pmid: 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.2015.07.062 pmid: 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.2016.08.023 pmid: 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/s00330-015-3814-0 pmid: 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.2017.08.134 pmid: 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 N₀ pulmonary adenocarcinoma with ALK rearrangement[J]. Cancer Manag Res, 2018,10:2117-2124. DOI: 10.2147/CMAR.S147569. doi: 10.2147/CMAR.S147569 pmid: 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.0100244 pmid: 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-17-0122 pmid: 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.2015.11.002 pmid: 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.2018.11.032 pmid: 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