Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features

Abstract To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups b...

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Autores principales:	Harsh Patel, David M. Vock, G. Elisabeta Marai, Clifton D. Fuller, Abdallah S. R. Mohamed, Guadalupe Canahuate
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7839f9f79de3480ebfb1a1b48ee86d6d
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spelling	oai:doaj.org-article:7839f9f79de3480ebfb1a1b48ee86d6d2021-12-02T18:34:13ZOropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features10.1038/s41598-021-92072-82045-2322https://doaj.org/article/7839f9f79de3480ebfb1a1b48ee86d6d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92072-8https://doaj.org/toc/2045-2322Abstract To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan–Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val < 0.0001). When compared to the models trained using clinical data only, the inclusion of the cluster label improves AUC test performance from .62 to .79 and from .66 to .80 for OS and RFS, respectively. The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and offers comparable performance to the models including raw radiomic features.Harsh PatelDavid M. VockG. Elisabeta MaraiClifton D. FullerAbdallah S. R. MohamedGuadalupe CanahuateNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Harsh Patel David M. Vock G. Elisabeta Marai Clifton D. Fuller Abdallah S. R. Mohamed Guadalupe Canahuate Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
description	Abstract To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan–Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val < 0.0001). When compared to the models trained using clinical data only, the inclusion of the cluster label improves AUC test performance from .62 to .79 and from .66 to .80 for OS and RFS, respectively. The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and offers comparable performance to the models including raw radiomic features.
format	article
author	Harsh Patel David M. Vock G. Elisabeta Marai Clifton D. Fuller Abdallah S. R. Mohamed Guadalupe Canahuate
author_facet	Harsh Patel David M. Vock G. Elisabeta Marai Clifton D. Fuller Abdallah S. R. Mohamed Guadalupe Canahuate
author_sort	Harsh Patel
title	Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
title_short	Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
title_full	Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
title_fullStr	Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
title_full_unstemmed	Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
title_sort	oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7839f9f79de3480ebfb1a1b48ee86d6d
work_keys_str_mv	AT harshpatel oropharyngealcancerpatientstratificationusingrandomforestbasedlearningoverhighdimensionalradiomicfeatures AT davidmvock oropharyngealcancerpatientstratificationusingrandomforestbasedlearningoverhighdimensionalradiomicfeatures AT gelisabetamarai oropharyngealcancerpatientstratificationusingrandomforestbasedlearningoverhighdimensionalradiomicfeatures AT cliftondfuller oropharyngealcancerpatientstratificationusingrandomforestbasedlearningoverhighdimensionalradiomicfeatures AT abdallahsrmohamed oropharyngealcancerpatientstratificationusingrandomforestbasedlearningoverhighdimensionalradiomicfeatures AT guadalupecanahuate oropharyngealcancerpatientstratificationusingrandomforestbasedlearningoverhighdimensionalradiomicfeatures
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Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features

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