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Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques
Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust mac...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517003/ https://www.ncbi.nlm.nih.gov/pubmed/37737249 http://dx.doi.org/10.1038/s41598-023-41353-5 |
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author | Vijithananda, Sahan M. Jayatilake, Mohan L. Gonçalves, Teresa C. Rato, Luis M. Weerakoon, Bimali S. Kalupahana, Tharindu D. Silva, Anil D. Dissanayake, Karuna Hewavithana, P. B. |
author_facet | Vijithananda, Sahan M. Jayatilake, Mohan L. Gonçalves, Teresa C. Rato, Luis M. Weerakoon, Bimali S. Kalupahana, Tharindu D. Silva, Anil D. Dissanayake, Karuna Hewavithana, P. B. |
author_sort | Vijithananda, Sahan M. |
collection | PubMed |
description | Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment. |
format | Online Article Text |
id | pubmed-10517003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105170032023-09-24 Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques Vijithananda, Sahan M. Jayatilake, Mohan L. Gonçalves, Teresa C. Rato, Luis M. Weerakoon, Bimali S. Kalupahana, Tharindu D. Silva, Anil D. Dissanayake, Karuna Hewavithana, P. B. Sci Rep Article Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment. Nature Publishing Group UK 2023-09-22 /pmc/articles/PMC10517003/ /pubmed/37737249 http://dx.doi.org/10.1038/s41598-023-41353-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Vijithananda, Sahan M. Jayatilake, Mohan L. Gonçalves, Teresa C. Rato, Luis M. Weerakoon, Bimali S. Kalupahana, Tharindu D. Silva, Anil D. Dissanayake, Karuna Hewavithana, P. B. Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques |
title | Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques |
title_full | Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques |
title_fullStr | Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques |
title_full_unstemmed | Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques |
title_short | Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques |
title_sort | texture feature analysis of mri-adc images to differentiate glioma grades using machine learning techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517003/ https://www.ncbi.nlm.nih.gov/pubmed/37737249 http://dx.doi.org/10.1038/s41598-023-41353-5 |
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