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Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions.

I. Tunali,L. Hall,S. Napel,Dmitry Cherezov,A. Guvenis,R. Gillies,M. Schabath

Published 2019 in Medical Physics (Lancaster)

ABSTRACT

PURPOSE Recent efforts have demonstrated that radiomic features extracted from the peritumoral region, the area surrounding the tumor parenchyma, have clinical utility in various cancer types. However, as like any radiomic features, peritumoral features may also be unstable and/or non-reproducible. Hence, the purpose of this study was to assess the stability and reproducibility of computed tomography (CT) radiomic features extracted from the peritumoral regions of lung lesions where stability was defined as the consistency of a feature by different segmentations, and reproducibility was defined as the consistency of a feature to image acquisition. METHODS Stability was measured utilizing the "Moist run" dataset and reproducibility was measured utilizing the Reference Image Database to Evaluate Therapy Response test-retest dataset. Peritumoral radiomic features were extracted from incremental distances of 3-12 mm outside the tumor parenchyma segmentation. A total of 264 statistical, histogram and texture radiomic features were assessed from the selected peritumoral region-of-interests. All features (except wavelet texture features) were extracted using standardized algorithms defined by the Image Biomarker Standardization Initiative. Stability and reproducibility of features were assessed using concordance correlation coefficient. The clinical utility of stable and reproducible peritumoral features were tested in three previously published lung cancer datasets using overall survival as the endpoint. RESULTS Features found to be stable and reproducible, regardless of the peritumoral distances, included statistical, histogram and a subset of texture features suggesting that these features are less affected by changes size or shape differences of the peritumoral region due to different segmentations and image acquisitions. The stability and reproducibility of 3D Laws and wavelet texture features were inconsistent across all peritumoral distances. The analyses also revealed that a subset of features were consistently stable irrespective of the initial parameters (e.g., seed point) for a given segmentation algorithm. No significant differences were found for stability for features that were extracted from region-of-interests (ROIs) bounded by a lung parenchyma mask versus ROIs that were not bounded by a lung parenchyma mask (i.e., peritumoral regions that were allowed to extend outside of lung parenchyma). After testing the clinical utility of peritumoral features, stable and reproducible features were shown to be more likely to create repeatable models than unstable and non-reproducible features. CONCLUSIONS This study identified a subset of stable and reproducible CT radiomic features extracted from the peritumoral region of lung lesions. The stable and reproducible features identified in this study could be applied to a feature selection pipeline for CT radiomic analyses. According to our findings, top performing features in models for overall survival are most likely to be stable and reproducible hence, it may be best practice to utilize them to achieve repeatable studies and reduce the creation of overfit models.

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