Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers

doi:10.2147/OTT.S257798

	Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers
	Ma, De-Ning 1,2; Gao, Xin-Yi 2,3; Dan, Yi-Bo 4; Zhang, An-Ni 2,3; Wang, Wei-Jun 2,3; Yang, Guang 4; Zhu, Hong-Zhou 2,3
刊名	ONCOTARGETS AND THERAPY
	2020
卷号	13
关键词	radiogenomics SVM non-small cell lung cancer anaplastic lymphoma kinase epidermal growth factor receptor
ISSN号	1178-6930
DOI	10.2147/OTT.S257798
通讯作者	Yang, Guang(gyang@phy.ecnu.edu.cn) ; Zhu, Hong-Zhou(715040192@qq.com)
英文摘要	Purpose: To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively. Materials and Methods: This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 pre-contrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPV of pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538-0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630-0.9567), 76.92%, 83.33%, 71.43%, and 86.96%. Conclusion: Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard post-contrast CT radiomics biomarkers produced superior performance compared with that of pre-contrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.
资助项目	National Natural Science Foundation of China[81702371] ; Natural Science Foundation of Zhejiang Province[LY20H160004]
WOS关键词	CANCER PATIENTS ; CLINICORADIOLOGIC CHARACTERISTICS ; ALK REARRANGEMENT ; EGFR ; FEATURES ; CRIZOTINIB ; INHIBITORS ; MUTATIONS ; SIGNATURE ; NOMOGRAM
WOS研究方向	Biotechnology & Applied Microbiology ; Oncology
语种	英语
出版者	DOVE MEDICAL PRESS LTD
WOS记录号	WOS:000550183300002
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Zhejiang Province
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/102967]
专题	中国科学院合肥物质科学研究院
通讯作者	Yang, Guang; Zhu, Hong-Zhou
作者单位	1.Univ Chinese Acad Sci, Zhejiang Canc Hosp, Dept Colorectal Surg, Canc Hosp, Hangzhou 310022, Zhejiang, Peoples R China 2.Chinese Acad Sci, Inst Canc & Basic Med ICBM, Hangzhou 310022, Zhejiang, Peoples R China 3.Univ Chinese Acad Sci, Zhejiang Canc Hosp, Canc Hosp, Dept Radiol, Hangzhou 310022, Zhejiang, Peoples R China 4.Shanghai Key Lab Magnet Resonance, Dept Phys, 3663 North Zhongshan Rd, Shanghai 200062, Peoples R China
推荐引用方式 GB/T 7714	Ma, De-Ning,Gao, Xin-Yi,Dan, Yi-Bo,et al. Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers[J]. ONCOTARGETS AND THERAPY,2020,13.
APA	Ma, De-Ning.,Gao, Xin-Yi.,Dan, Yi-Bo.,Zhang, An-Ni.,Wang, Wei-Jun.,...&Zhu, Hong-Zhou.(2020).Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers.ONCOTARGETS AND THERAPY,13.
MLA	Ma, De-Ning,et al."Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers".ONCOTARGETS AND THERAPY 13(2020).