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Background: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC)., Methods: Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV)., Results: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively., Conclusions: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

Background: Radiomics provides promising opportunities in cancer diagnosis, endowing medical imaging with an increasingly important role in analyzing tumor phenotypes. Positron emission computed tomography (PET) imaging can detect functional changes before they become morphologically evident on computed tomography (CT) imaging. The aim of this study was to explore the feasibility of using quantitative PET radiomic and clinical features to identify subtypes of non-small-cell lung cancer (NSCLC)., Methods: In this study, one hundred patients who had been diagnosed with histologically confirmed NSCLC were collected retrospectively, including 61 patients with adenocarcinoma (ADC) and 39 patients with squamous cell carcinoma (SqCC). Then, the gross tumor volume (GTV) was delineated on PET images. A total of 107 features were extracted, which included 60 texture features and 47 metabolic features. The least absolute shrinkage and selection operator (LASSO) was used to select the optimal feature set, which was considered to be the best predictable features. Meanwhile, we analyzed the differences of selected features between two tumor types. Classification models were built by multivariable logistic regression analysis with three settings, namely: (I) radiomic features; (II) clinical features (smoking, age, sex, tumor size, T stage and N stage); and (III) radiomic features combined with clinical features. Finally, the area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the classification models., Results: Five out of 107 features were selected as the optimal feature set, which included four texture features and one metabolic feature. Significant differences were observed from these five features between ADC and SqCC subtypes (P<0.05). The radiomics features combined with clinical features model showed higher classification performance (AUC =0.781, sensitivity =1.000, specificity =0.700, accuracy =0.885) than the radiomic model (AUC =0.700, sensitivity =0.938, specificity =0.600, accuracy =0.808) and clinical features model (AUC =0.728, sensitivity =0.625, specificity =1.000, accuracy =0.769) in the validation datasets., Conclusions: The 18 F-FDG PET radiomic classification model is a promising and applicable approach for identifying subtypes of NSCLC, which may serve as a complementary tool to help doctors with clinical decisions., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr.2019.08.20). The authors have no conflicts of interest to declare., (2019 Translational Cancer Research. All rights reserved.)