OncoTargets and Therapy (Jul 2020)
Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers
Abstract
De-Ning Ma,1,2,* Xin-Yi Gao,2,3,* Yi-Bo Dan,4,* An-Ni Zhang,2,3 Wei-Jun Wang,2,3 Guang Yang,4 Hong-Zhou Zhu2,3 1Department of Colorectal Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang Province, People’s Republic of China; 2Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, People’s Republic of China; 3Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang Province, People’s Republic of China; 4Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hong-Zhou ZhuCancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), 1# East Banshan Road, Hangzhou City, Zhejiang Province 310022, People’s Republic of ChinaTel/ Fax +86 571-88122222Email [email protected] YangDepartment of Physics, Shanghai Key Laboratory of Magnetic Resonance, 3663# North Zhongshan Road, Shanghai 200062, People’s Republic of ChinaTel/ Fax +86 21-62233873Email [email protected]: 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.Keywords: radiogenomics, SVM, non-small cell lung cancer, anaplastic lymphoma kinase, epidermal growth factor receptor
