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Conflicts of Interest:The authors declare no potential conflicts of interest. Abstract PurposeConsiderable progress has been made in assessment and management of NSCLC patients based on mutation status in the EGFR and KRAS. At the same time, NSCLC management through KRAS and EGFR mutation profiling faces some challenges. In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on low dose CT, diagnostic CT, PET modalities radiomic features and machine learning (ML) algorithms. MethodsOur study involved NSCLC cancer patient with 186 PET, and 175 low dose CT and CTD images. More than twenty thousand radiomic features from different image-feature sets were extracted. Conventional clinically used standard uptake value (SUV) parameters were also obtained for PET images. Feature value was normalized to obtain Z-scores, followed by student t-test students for comparison, high correlated features were eliminated and the False discovery rate (FDR) correction were performed and q-value were reported for univariate analysis. Six feature selection methods and twelve classifiers were used to predict gene status in patient. We performed 10-fold cross-validation for model tuning to improve robustness and all model evaluation was reported on independent validation sets (68 patients). The mean area under the receiver operator characteristic (ROC) curve (AUC) was obtained for performance evaluation. ResultsThe best predictive power of conventional PET parameters was achieved by SUVpeak (AUC: 0.69, P-value = 0.0002) and MTV (AUC: 0.55, P-value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of extracted radiomics features improved prediction power up to AUC: 75 (q-value: 0.003, Short Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and AUC: 0.71 (q-value 0.00005, The Large Dependence Low Gray Level Emphasis from GLDM in LOG preprocessed image of CTD sigma value 5) for EGFR and KRAS, respectively. Furthermore, the machine learning algorithm improved the perdition power up to AUC: 0.82 for EGFR (LOG preprocessed of PET image set with sigma 3 with VT feature selector and SGD classifier) and AUC: 0.83 for KRAS (CT image set with sigma 3.5 with SM feature selector and SGD classifier). ConclusionOur findings demonstrated that non-invasive and reliable radiomics analysis can be successfully used to predict EGFR and KRAS mutation status in NSCLC patients. We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients, and showed that they have more predictive power than conventional imaging parameters.This study was conducted on 211 NSCLC cancer patients with available imaging and genomic data. Imaging modalities, including diagnostic CT (CTD) and PET/CT (i.e., low-dose CT (CT) used for PET attenuation correction and the PET image) for all patients were obtained (29-32) . All images we...

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