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Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)

Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative di...

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Autores principales: Haniff, Nurin Syazwina Mohd, Abdul Karim, Muhammad Khalis, Osman, Nurul Huda, Saripan, M Iqbal, Che Isa, Iza Nurzawani, Ibahim, Mohammad Johari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468357/
https://www.ncbi.nlm.nih.gov/pubmed/34573915
http://dx.doi.org/10.3390/diagnostics11091573
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author Haniff, Nurin Syazwina Mohd
Abdul Karim, Muhammad Khalis
Osman, Nurul Huda
Saripan, M Iqbal
Che Isa, Iza Nurzawani
Ibahim, Mohammad Johari
author_facet Haniff, Nurin Syazwina Mohd
Abdul Karim, Muhammad Khalis
Osman, Nurul Huda
Saripan, M Iqbal
Che Isa, Iza Nurzawani
Ibahim, Mohammad Johari
author_sort Haniff, Nurin Syazwina Mohd
collection PubMed
description Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR images of HCC. Four types of features extraction were used for this study, which are tumour intensity, shape feature, textural feature and wavelet feature. A total of 662 radiomic features were extracted from manual and semi-automatic segmentation and compared using intra-class relation coefficient (ICC). Radiomic features extracted using semi-automatic segmentation utilized flood filling algorithm from 3D-slicer had significantly higher reproducibility (average ICC = 0.952 ± 0.009, p < 0.05) compared with features extracted from manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). Moreover, features extracted from semi-automatic segmentation were more robust compared to manual segmentation. This study shows that semi-automatic segmentation from 3D-Slicer is a better alternative to the manual segmentation, as they can produce more robust and reproducible radiomic features.
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spelling pubmed-84683572021-09-27 Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC) Haniff, Nurin Syazwina Mohd Abdul Karim, Muhammad Khalis Osman, Nurul Huda Saripan, M Iqbal Che Isa, Iza Nurzawani Ibahim, Mohammad Johari Diagnostics (Basel) Article Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR images of HCC. Four types of features extraction were used for this study, which are tumour intensity, shape feature, textural feature and wavelet feature. A total of 662 radiomic features were extracted from manual and semi-automatic segmentation and compared using intra-class relation coefficient (ICC). Radiomic features extracted using semi-automatic segmentation utilized flood filling algorithm from 3D-slicer had significantly higher reproducibility (average ICC = 0.952 ± 0.009, p < 0.05) compared with features extracted from manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). Moreover, features extracted from semi-automatic segmentation were more robust compared to manual segmentation. This study shows that semi-automatic segmentation from 3D-Slicer is a better alternative to the manual segmentation, as they can produce more robust and reproducible radiomic features. MDPI 2021-08-30 /pmc/articles/PMC8468357/ /pubmed/34573915 http://dx.doi.org/10.3390/diagnostics11091573 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Haniff, Nurin Syazwina Mohd
Abdul Karim, Muhammad Khalis
Osman, Nurul Huda
Saripan, M Iqbal
Che Isa, Iza Nurzawani
Ibahim, Mohammad Johari
Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)
title Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)
title_full Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)
title_fullStr Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)
title_full_unstemmed Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)
title_short Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)
title_sort stability and reproducibility of radiomic features based various segmentation technique on mr images of hepatocellular carcinoma (hcc)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468357/
https://www.ncbi.nlm.nih.gov/pubmed/34573915
http://dx.doi.org/10.3390/diagnostics11091573
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