非小细胞肺癌影像学特征与基因表达间相关性的探索性研究

doi:10.3969/j.issn.0258-8021.2020.06.003

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Abstract Radiogenomics combines the complementary advantages of radiomics and genomics by mining the association of them to guide the development of individualized treatment regimens, prognosis evaluation and efficacy detection for different patients. This paper established the mapping between quantitative characteristics of CT images and gene expression for non-small cell lung cancer (NSCLC). Firstly, the tumor regions in the corresponding CT images were segmented and features were extracted. We selected 66 kinds of three-dimensional quantitative features as the imaging feature set of the tumor area. Secondly, the first principal component was obtained as the representative of the clustering results with similar expression profiles after preprocessing and clustering the original genetic data by using genomics data analysis process. Finally, the algorithm about significance analysis of microarray was used to explore the correlation between imaging features and gene expression. We also carried out the enrichment analysis of gene sets and established the prediction models. The 26 cases of NSCLC image data from this study were selected from the Cancer Imaging Archive (TCIA) and the corresponding genetic data were derived from the Gene Expression Omnibus (GEO). Analysis of these data revealed a significant association of 126 pairs (q<0.05). Prediction models were established for 29 sets of genes in the obtained results. In addition, the updated 211 sets of data from TCIA were used to verify the prediction model with the predicted metagenomic significance in 10 of the 29 groups, whose prediction accuracy was more than 70%. In addition, 10 predictive models with prediction accuracy of more than 70% and biological significance were verified by 211 groups of updated data in TCIA. The final prediction accuracy was 35.48～80.85% and the accuracy of six of the 10 prediction models was above 70%. These experimental results showed that the specific image features or their combination could be used as image markers of gene expression.

Key words： radiogenomics non-small cell lung cancer (NSCLC) imaging features gene expression

Received: 07 January 2019

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R318

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	Articles by authors
	Wang Ting
	Gong Jing
	Duan Huihong
	Wang Lijia
	Nie Shengdong

Cite this article:

Wang Ting,Gong Jing,Duan Huihong, et al. Study on the Correlation between Imaging Features and Gene Expression in Non-small Cell Lung Cancer[J]. Chinese Journal of Biomedical Engineering, 2020, 39(6): 667-675.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.06.003 OR http://cjbme.csbme.org/EN/Y2020/V39/I6/667

[1] Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging [J]. European Journal of Radiology, 2009, 70(2): 232-241.
[2] Mazurowski MA. Radiogenomics: what it is and why it is important [J]. Journal of the American College of Radiology, 2015, 12(8): 862-866.
[3] Kuo MD, Jamshidi N. Behind the numbers: Decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations [J]. Radiology, 2014, 270(2): 320-325.
[4] Yachida S, Jones S, Bozic I, et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer [J]. Nature, 2010, 467(7319): 1114-1117.
[5] Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing [J]. New England Journal of Medicine, 2012, 366(10): 883-892.
[6] Sottoriva A, Spiteri I, Piccirillo SGM, et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics [J]. Proceedings of the National Academy of Sciences, 2013, 110(10): 4009-4014.
[7] Shedden K, Taylor JMG, Enkemann SA, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study [J]. Nature Medicine, 2008, 14(8): 822-827.
[8] Chung CH, Bernard PS, Perou CM. Molecular portraits and the family tree of cancer [J]. Nature Genetics, 2002, 32(Supp): 533-540.
[9] Carter SL, Eklund AC, Kohane IS, et al. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers [J]. Nature Genetics, 2006, 38(9): 1043-1048.
[10] Segal E, Friedman N, Kaminski N, et al. From signatures to models: understanding cancer using microarrays [J]. Nature Genetics, 2005, 37(6s): S38-S45.
[11] Feng Hongxiang, Zhang Zhenrong, Qing Xin, et al. Promoter methylation of APC and RAR-β genes as prognostic markers in non-small cell lung cancer (NSCLC) [J]. Experimental and Molecular Pathology, 2016, 100(1): 109-113.
[12] Paez JG, Jnne PA, Lee JC, et al. EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy [J]. Science, 2004, 304(5676): 1497-1500.
[13] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016 [J]. CA: A Cancer Journal for Clinicians, 2016, 66(1): 7-30.
[14] 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]. Annals of Thoracic Surgery, 2016, 101(2): 473-480.
[15] Nair VS, Gevaert O, Davidzon G, et al. Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer [J]. Cancer Research, 2012, 72(15): 3725-3734.
[16] Rizzo S, Petrella F, Buscarino V, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer [J]. European Radiology, 2016, 26(1): 32-42.
[17] 章雷. 非小细胞肺癌影像学特征与EGFR基因突变的关系研究 [D]. 北京: 中国人民解放军军事医学科学院, 2016.
[18] 章雷, 李妹, 乔鹏岗, 等. 周围型非小细胞肺癌CT特征与EGFR基因突变的关系研究 [J]. 人民军医, 2016, (9): 943-945.
[19] 畅润笙. 肺癌CT影像特征与EGFR基因突变之间的关联分析 [D]. 沈阳: 东北大学, 2015.
[20] Gevaert O, Xu J, Hoang CD, et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results [J]. Radiology, 2012, 264(2): 387-396.
[21] Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository [J]. Journal of Digital Imaging, 2013, 26(6): 1045-1057.
[22] Gong Jing, Liu Jiyu, Wang Lijia, et al. Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier [J]. Physica Medica, 2016, 32(12): 1502-1509.
[23] Ye Xujiong, Lin Xinyu, Dehmeshki J, et al. Shape-based computer-aided detection of lung nodules in thoracic CT images [J]. IEEE Transactions on Biomedical Engineering, 2009, 56(7): 1810-1820.
[24] Choi WJ, Choi TS. Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor [J]. Computer Methods and Programs in Biomedicine, 2014, 113(1): 37-54.
[25] Retico A, Delogu P, Fantacci ME, et al. Lung nodule detection in low-dose and thin-slice computed tomography [J]. Computers in Biology and Medicine, 2008, 38(4): 525-534.
[26] Haralick RM, Shanmugam K. Textural features for image classification [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610-621.
[27] Galloway MM. Texture analysis using gray level run lengths [J]. Computer Graphics and Image Processing, 1975, 4(2): 172-179.
[28] Amadasun M, King R. Textural features corresponding to textural properties [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(5): 1264-1274.
[29] Thibault G, Fertil B, Navarro C, et al. Texture Indexes and Gray Level Size Zone Matrix Application to Cell Nuclei Classification [C] //The 10th International Conference on Pattern Recognition and Information Processing. Minsk: PRIP, 2009: 140-145.
[30] Sturn A, Quackenbush J, Trajanoski Z. Genesis: Cluster analysis of microarray data [J]. Bioinformatics, 2002, 18(1): 207-208.
[31] Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data [J]. Bioinformatics, 2001, 17(9): 763-774.
[32] Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response [J]. Proceedings of the National Academy of Sciences, 2001, 98(9): 5116-5121.
[33] Huang Dawei, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources [J]. Nature Protocols, 2008, 4(1): 44-57.
[34] Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent [J]. Journal of Statistical Software, 2010, 33(1): 1-22.
[35] Park MY, Hastie T. L1-regularization path algorithm for generalized linear models [J]. Journal of the Royal Statistical Society, 2007, 69(4):659-677.
[36] Tibshirani R. SAMR: Significance analysis of microarrays [EB/OL]. http://CRAN.R-project.org/package=samr, 2018-10-16/2019-01-10.
[37] Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [J]. Nature Communications, 2014, 5(1): 1301-1312.
[38] Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing [J]. New England journal of medicine, 2012, 366(10): 883-892.
[39] Zhang J, Fujimoto J, Zhang J, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing [J]. Science, 2014, 346(6206): 256-259.
[40] Ateeq B, Bhatia VS. Molecular discriminators of racial disparities in prostate cancer [J]. Trends in Cancer, 2016, 2(3): 116.
[41] 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, 2017, 286(1): 307-315.