Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics

Abstract Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-c...

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Autores principales:	Sarv Priya, Yanan Liu, Caitlin Ward, Nam H. Le, Neetu Soni, Ravishankar Pillenahalli Maheshwarappa, Varun Monga, Honghai Zhang, Milan Sonka, Girish Bathla
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d7acdcc65ddc4b73a1ab61f4d0524d3f
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spelling	oai:doaj.org-article:d7acdcc65ddc4b73a1ab61f4d0524d3f2021-12-02T15:45:26ZMachine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics10.1038/s41598-021-90032-w2045-2322https://doaj.org/article/d7acdcc65ddc4b73a1ab61f4d0524d3f2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90032-whttps://doaj.org/toc/2045-2322Abstract Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.Sarv PriyaYanan LiuCaitlin WardNam H. LeNeetu SoniRavishankar Pillenahalli MaheshwarappaVarun MongaHonghai ZhangMilan SonkaGirish BathlaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sarv Priya Yanan Liu Caitlin Ward Nam H. Le Neetu Soni Ravishankar Pillenahalli Maheshwarappa Varun Monga Honghai Zhang Milan Sonka Girish Bathla Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics
description	Abstract Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.
format	article
author	Sarv Priya Yanan Liu Caitlin Ward Nam H. Le Neetu Soni Ravishankar Pillenahalli Maheshwarappa Varun Monga Honghai Zhang Milan Sonka Girish Bathla
author_facet	Sarv Priya Yanan Liu Caitlin Ward Nam H. Le Neetu Soni Ravishankar Pillenahalli Maheshwarappa Varun Monga Honghai Zhang Milan Sonka Girish Bathla
author_sort	Sarv Priya
title	Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics
title_short	Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics
title_full	Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics
title_fullStr	Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics
title_full_unstemmed	Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics
title_sort	machine learning based differentiation of glioblastoma from brain metastasis using mri derived radiomics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d7acdcc65ddc4b73a1ab61f4d0524d3f
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Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics

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