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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Published in:Diagnostics
Main Author: Haniff N.S.M.; Karim M.K.A.; Osman N.H.; Saripan M.I.; Isa I.N.C.; Ibahim M.J.
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114466253&doi=10.3390%2fdiagnostics11091573&partnerID=40&md5=e80fa906491a53b3056921dfc2efb714
id 2-s2.0-85114466253
spelling 2-s2.0-85114466253
Haniff N.S.M.; Karim M.K.A.; Osman N.H.; Saripan M.I.; Isa I.N.C.; Ibahim M.J.
Stability and reproducibility of radiomic features based various segmentation technique on mr images of hepatocellular carcinoma (Hcc)
2021
Diagnostics
11
9
10.3390/diagnostics11091573
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114466253&doi=10.3390%2fdiagnostics11091573&partnerID=40&md5=e80fa906491a53b3056921dfc2efb714
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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Multidisciplinary Digital Publishing Institute (MDPI)
20754418
English
Article
All Open Access; Gold Open Access; Green Open Access
author Haniff N.S.M.; Karim M.K.A.; Osman N.H.; Saripan M.I.; Isa I.N.C.; Ibahim M.J.
spellingShingle Haniff N.S.M.; Karim M.K.A.; Osman N.H.; Saripan M.I.; Isa I.N.C.; Ibahim M.J.
Stability and reproducibility of radiomic features based various segmentation technique on mr images of hepatocellular carcinoma (Hcc)
author_facet Haniff N.S.M.; Karim M.K.A.; Osman N.H.; Saripan M.I.; Isa I.N.C.; Ibahim M.J.
author_sort Haniff N.S.M.; Karim M.K.A.; Osman N.H.; Saripan M.I.; Isa I.N.C.; Ibahim M.J.
title 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_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_sort Stability and reproducibility of radiomic features based various segmentation technique on mr images of hepatocellular carcinoma (Hcc)
publishDate 2021
container_title Diagnostics
container_volume 11
container_issue 9
doi_str_mv 10.3390/diagnostics11091573
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114466253&doi=10.3390%2fdiagnostics11091573&partnerID=40&md5=e80fa906491a53b3056921dfc2efb714
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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
publisher Multidisciplinary Digital Publishing Institute (MDPI)
issn 20754418
language English
format Article
accesstype All Open Access; Gold Open Access; Green Open Access
record_format scopus
collection Scopus
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