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Impact of segmentation and discretization on radiomic features in (68)Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

OBJECTIVE: To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from (68)Ga-DOTA-TOC PET images in patients with neuroendocrine tumors. METHODS: Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by...

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Autores principales: Liberini, Virginia, De Santi, Bruno, Rampado, Osvaldo, Gallio, Elena, Dionisi, Beatrice, Ceci, Francesco, Polverari, Giulia, Thuillier, Philippe, Molinari, Filippo, Deandreis, Désirée
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914329/
https://www.ncbi.nlm.nih.gov/pubmed/33638729
http://dx.doi.org/10.1186/s40658-021-00367-6
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author Liberini, Virginia
De Santi, Bruno
Rampado, Osvaldo
Gallio, Elena
Dionisi, Beatrice
Ceci, Francesco
Polverari, Giulia
Thuillier, Philippe
Molinari, Filippo
Deandreis, Désirée
author_facet Liberini, Virginia
De Santi, Bruno
Rampado, Osvaldo
Gallio, Elena
Dionisi, Beatrice
Ceci, Francesco
Polverari, Giulia
Thuillier, Philippe
Molinari, Filippo
Deandreis, Désirée
author_sort Liberini, Virginia
collection PubMed
description OBJECTIVE: To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from (68)Ga-DOTA-TOC PET images in patients with neuroendocrine tumors. METHODS: Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUV(max) fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COV(L)). The RFs’ correlation with volume and SUV(max) was analyzed by calculating Pearson’s correlation coefficients. RESULTS: DSC mean value was 0.75 ± 0.11 (0.45–0.92) between SAEB and operators and 0.78 ± 0.09 (0.36–0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUV(max) threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUV(max) thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUV(max). CONCLUSIONS: RFs robustness to manual segmentation resulted higher in NET (68)Ga-DOTA-TOC images compared to (18)F-FDG PET/CT images. Forty percent SUV(max) thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-021-00367-6.
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spelling pubmed-79143292021-03-15 Impact of segmentation and discretization on radiomic features in (68)Ga-DOTA-TOC PET/CT images of neuroendocrine tumor Liberini, Virginia De Santi, Bruno Rampado, Osvaldo Gallio, Elena Dionisi, Beatrice Ceci, Francesco Polverari, Giulia Thuillier, Philippe Molinari, Filippo Deandreis, Désirée EJNMMI Phys Original Research OBJECTIVE: To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from (68)Ga-DOTA-TOC PET images in patients with neuroendocrine tumors. METHODS: Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUV(max) fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COV(L)). The RFs’ correlation with volume and SUV(max) was analyzed by calculating Pearson’s correlation coefficients. RESULTS: DSC mean value was 0.75 ± 0.11 (0.45–0.92) between SAEB and operators and 0.78 ± 0.09 (0.36–0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUV(max) threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUV(max) thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUV(max). CONCLUSIONS: RFs robustness to manual segmentation resulted higher in NET (68)Ga-DOTA-TOC images compared to (18)F-FDG PET/CT images. Forty percent SUV(max) thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-021-00367-6. Springer International Publishing 2021-02-27 /pmc/articles/PMC7914329/ /pubmed/33638729 http://dx.doi.org/10.1186/s40658-021-00367-6 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Research
Liberini, Virginia
De Santi, Bruno
Rampado, Osvaldo
Gallio, Elena
Dionisi, Beatrice
Ceci, Francesco
Polverari, Giulia
Thuillier, Philippe
Molinari, Filippo
Deandreis, Désirée
Impact of segmentation and discretization on radiomic features in (68)Ga-DOTA-TOC PET/CT images of neuroendocrine tumor
title Impact of segmentation and discretization on radiomic features in (68)Ga-DOTA-TOC PET/CT images of neuroendocrine tumor
title_full Impact of segmentation and discretization on radiomic features in (68)Ga-DOTA-TOC PET/CT images of neuroendocrine tumor
title_fullStr Impact of segmentation and discretization on radiomic features in (68)Ga-DOTA-TOC PET/CT images of neuroendocrine tumor
title_full_unstemmed Impact of segmentation and discretization on radiomic features in (68)Ga-DOTA-TOC PET/CT images of neuroendocrine tumor
title_short Impact of segmentation and discretization on radiomic features in (68)Ga-DOTA-TOC PET/CT images of neuroendocrine tumor
title_sort impact of segmentation and discretization on radiomic features in (68)ga-dota-toc pet/ct images of neuroendocrine tumor
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914329/
https://www.ncbi.nlm.nih.gov/pubmed/33638729
http://dx.doi.org/10.1186/s40658-021-00367-6
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