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Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients.

Published version
Peer-reviewed

Type

Article

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Authors

Mottola, Margherita 
Sanchez, Lorena Escudero 
Klatte, Tobias 

Abstract

Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.

Description

Keywords

Algorithms, Carcinoma, Renal Cell, Humans, Image Processing, Computer-Assisted, Kidney, Kidney Neoplasms, Reproducibility of Results, Tomography, X-Ray Computed

Journal Title

Sci Rep

Conference Name

Journal ISSN

2045-2322
2045-2322

Volume Title

11

Publisher

Springer Science and Business Media LLC
Sponsorship
Cancer Research UK (C96/A25177)
National Institute for Health and Care Research (IS-BRC-1215-20014)
EPSRC (EP/T017961/1)