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Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI

Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of t...

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Autores principales: Ramli, Zarina, Karim, Muhammad Khalis Abdul, Effendy, Nuraidayani, Abd Rahman, Mohd Amiruddin, Kechik, Mohd Mustafa Awang, Ibahim, Mohamad Johari, Haniff, Nurin Syazwina Mohd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777485/
https://www.ncbi.nlm.nih.gov/pubmed/36553132
http://dx.doi.org/10.3390/diagnostics12123125
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author Ramli, Zarina
Karim, Muhammad Khalis Abdul
Effendy, Nuraidayani
Abd Rahman, Mohd Amiruddin
Kechik, Mohd Mustafa Awang
Ibahim, Mohamad Johari
Haniff, Nurin Syazwina Mohd
author_facet Ramli, Zarina
Karim, Muhammad Khalis Abdul
Effendy, Nuraidayani
Abd Rahman, Mohd Amiruddin
Kechik, Mohd Mustafa Awang
Ibahim, Mohamad Johari
Haniff, Nurin Syazwina Mohd
author_sort Ramli, Zarina
collection PubMed
description Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features.
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spelling pubmed-97774852022-12-23 Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI Ramli, Zarina Karim, Muhammad Khalis Abdul Effendy, Nuraidayani Abd Rahman, Mohd Amiruddin Kechik, Mohd Mustafa Awang Ibahim, Mohamad Johari Haniff, Nurin Syazwina Mohd Diagnostics (Basel) Article Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features. MDPI 2022-12-12 /pmc/articles/PMC9777485/ /pubmed/36553132 http://dx.doi.org/10.3390/diagnostics12123125 Text en © 2022 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
Ramli, Zarina
Karim, Muhammad Khalis Abdul
Effendy, Nuraidayani
Abd Rahman, Mohd Amiruddin
Kechik, Mohd Mustafa Awang
Ibahim, Mohamad Johari
Haniff, Nurin Syazwina Mohd
Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI
title Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI
title_full Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI
title_fullStr Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI
title_full_unstemmed Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI
title_short Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI
title_sort stability and reproducibility of radiomic features based on various segmentation techniques on cervical cancer dwi-mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777485/
https://www.ncbi.nlm.nih.gov/pubmed/36553132
http://dx.doi.org/10.3390/diagnostics12123125
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