Abstract
ObjectivesTo propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs).Materials and methodsContrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers’ scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test.ResultsThe BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29).ConclusionThe proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.
Highlights
Introduction85% of lung cancer lesions are of the non-small cell lung cancer (NSCLC) subtype [2]
Lung cancer is the leading cause of cancer-related deaths worldwide [1]
Past studies have not investigated the feasibility of their model using datasets with wide variety of imaging parameters or patient populations [8,9], it was reported that conventional radiomic features can struggle to extract intrinsic image features that are robust to variations in computed tomography (CT) scanner or scanning parameters [11,12]
Summary
85% of lung cancer lesions are of the non-small cell lung cancer (NSCLC) subtype [2]. In a rapidly developing field of radiomics, researchers have been investigating the associations between medical images and patients’ prognostic information (including the EGFR mutation status) in a non-invasive manner under the assumption that particular somatic mutations of cancer lead remarkable phenotypes appearing on the medical images (Fig 1) [4,5,6,7]. Previous studies demonstrated underlying associations between the EGFR mutation status and intra-tumor heterogeneity on computed tomography (CT) images quantified using conventional radiomic features such as shape-, original image (OI)-, wavelet-decomposition (WD)-, and deep learning-based features [8,9,10]. Past studies have not investigated the feasibility of their model using datasets with wide variety of imaging parameters or patient populations [8,9], it was reported that conventional radiomic features can struggle to extract intrinsic image features that are robust to variations in CT scanner or scanning parameters [11,12]
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