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

Abstract

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.

Keywords: SVM; anaplastic lymphoma kinase; epidermal growth factor receptor; non-small cell lung cancer; radiogenomics.

Figure 1

Flowchart of this study. (A) Patients. (B) Image Acquisition. (C) ROI Segmentation. (D) Feature Extraction and Selection. (E) SVM Construction and Validation.

Figure 2

CT images obtained in a 49-year old woman with ALK rearrangement solid lung adenocarcinoma. Transverse mediastinal window, 5 mm slice thickness. (A and B) Pre-contrast scans. (C and D) Standard post-contrast scans.

Figure 3

Results of classifier 1. (A) The receiver-operating characteristic curve of classifier 1. (B) Selection of optimal features using ANOVA. (C) Contribution of selected radiomics features of classifier 1.

Figure 4

Results of classifier 2. (A) The receiver-operating characteristic curve of classifier 2. (B) Selection of optimal features using RFE. (C) Contribution of selected radiomics features of classifier 2.