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

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 int...

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Autores principales:	Margherita Mottola, Stephan Ursprung, Leonardo Rundo, Lorena Escudero Sanchez, Tobias Klatte, Iosif Mendichovszky, Grant D Stewart, Evis Sala, Alessandro Bevilacqua
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c0efd4e12f234fa7abb5a8e2518d129a
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spelling	oai:doaj.org-article:c0efd4e12f234fa7abb5a8e2518d129a2021-12-02T18:24:53ZReproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients10.1038/s41598-021-90985-y2045-2322https://doaj.org/article/c0efd4e12f234fa7abb5a8e2518d129a2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90985-yhttps://doaj.org/toc/2045-2322Abstract 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.Margherita MottolaStephan UrsprungLeonardo RundoLorena Escudero SanchezTobias KlatteIosif MendichovszkyGrant D StewartEvis SalaAlessandro BevilacquaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Margherita Mottola Stephan Ursprung Leonardo Rundo Lorena Escudero Sanchez Tobias Klatte Iosif Mendichovszky Grant D Stewart Evis Sala Alessandro Bevilacqua Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients
description	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.
format	article
author	Margherita Mottola Stephan Ursprung Leonardo Rundo Lorena Escudero Sanchez Tobias Klatte Iosif Mendichovszky Grant D Stewart Evis Sala Alessandro Bevilacqua
author_facet	Margherita Mottola Stephan Ursprung Leonardo Rundo Lorena Escudero Sanchez Tobias Klatte Iosif Mendichovszky Grant D Stewart Evis Sala Alessandro Bevilacqua
author_sort	Margherita Mottola
title	Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients
title_short	Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients
title_full	Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients
title_fullStr	Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients
title_full_unstemmed	Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients
title_sort	reproducibility of ct-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c0efd4e12f234fa7abb5a8e2518d129a
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Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients

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