Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma

Abstract We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradio...

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Autores principales:	Bum-Sup Jang, Seung Hyuck Jeon, Il Han Kim, In Ah Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cdb37370dbdc4f8f86062035714c7a2b
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spelling	oai:doaj.org-article:cdb37370dbdc4f8f86062035714c7a2b2021-12-02T15:08:05ZPrediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma10.1038/s41598-018-31007-22045-2322https://doaj.org/article/cdb37370dbdc4f8f86062035714c7a2b2018-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-31007-2https://doaj.org/toc/2045-2322Abstract We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were allocated to training (N = 59) and testing (N = 19) datasets, respectively. We developed a convolutional neural network combined with a long short-term memory ML structure. MRI data, which was 9 axial post-contrast T1-weighted images in our study, and clinical features were incorporated (Model 1). In the testing set, the trained Model 1 resulted in AUC of 0.83, AUPRC of 0.87, and F1-score of 0.74 using optimal threshold. The performance was superior to that of Model 2 (CNN-LSTM model with MRI data alone) and Model 3 (random forest model with clinical feature alone). The developed algorithm involving MRI data and clinical features could help making decision during follow-up of patients with GBM treated with GTR and concurrent CCRT.Bum-Sup JangSeung Hyuck JeonIl Han KimIn Ah KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bum-Sup Jang Seung Hyuck Jeon Il Han Kim In Ah Kim Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
description	Abstract We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were allocated to training (N = 59) and testing (N = 19) datasets, respectively. We developed a convolutional neural network combined with a long short-term memory ML structure. MRI data, which was 9 axial post-contrast T1-weighted images in our study, and clinical features were incorporated (Model 1). In the testing set, the trained Model 1 resulted in AUC of 0.83, AUPRC of 0.87, and F1-score of 0.74 using optimal threshold. The performance was superior to that of Model 2 (CNN-LSTM model with MRI data alone) and Model 3 (random forest model with clinical feature alone). The developed algorithm involving MRI data and clinical features could help making decision during follow-up of patients with GBM treated with GTR and concurrent CCRT.
format	article
author	Bum-Sup Jang Seung Hyuck Jeon Il Han Kim In Ah Kim
author_facet	Bum-Sup Jang Seung Hyuck Jeon Il Han Kim In Ah Kim
author_sort	Bum-Sup Jang
title	Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
title_short	Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
title_full	Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
title_fullStr	Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
title_full_unstemmed	Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
title_sort	prediction of pseudoprogression versus progression using machine learning algorithm in glioblastoma
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/cdb37370dbdc4f8f86062035714c7a2b
work_keys_str_mv	AT bumsupjang predictionofpseudoprogressionversusprogressionusingmachinelearningalgorithminglioblastoma AT seunghyuckjeon predictionofpseudoprogressionversusprogressionusingmachinelearningalgorithminglioblastoma AT ilhankim predictionofpseudoprogressionversusprogressionusingmachinelearningalgorithminglioblastoma AT inahkim predictionofpseudoprogressionversusprogressionusingmachinelearningalgorithminglioblastoma
_version_	1718388281529335808

Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma

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