Assessing the Robustness and Reproducibility of CT Radiomics Features in Non-small-cell Lung Carcinoma

The aim of this study was to investigate the robustness of radiomics features extracted from computed tomography (CT) images of patients affected by non-small-cell lung carcinoma (NSCLC). Specifically, the impact of manual segmentation on radiomics feature values and their variability were assessed. Therefore, 63 patients affected by squamous cell carcinoma (SCC) and adenocarcinoma (ADC) were retrospectively collected from a public dataset. Original segmentations (automated plus manual refinement approach) were provided together with CT images. Through the matRadiomics tool, manual segmentation of the volume of interest (VOI) was repeated by two training physicians and 107 features were extracted. Feature extraction was also performed using the original segmentations. Therefore, three datasets of extracted features were obtained and compared computing the difference percentage coefficient (DP) and the intraclass correlation coefficient (ICC). Moreover, feature reduction and selection on each dataset were performed using a hybrid descriptive inferential method and the differences among the three feature subsets were evaluated. Successively, three classification models were obtained using the Linear Discriminant Analysis (LDA) classifier. Validation was performed through 10 times repeated 5-fold stratified cross validation. As result, even if 87% features obtained an ICC > 0.8, showing robustness, an AVGDP (averaged DP) equal to 16.2% was observed between the datasets based on manual segmentation. Moreover, manual segmentation had an impact on the subsets of selected features, thus influencing study reproducibility and model explainability.