Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis

Abstract Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient’s preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two questions: can a single checkup predict the...

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Autores principales:	Oliver Profant, Zbyněk Bureš, Zuzana Balogová, Jan Betka, Zdeněk Fík, Martin Chovanec, Jan Voráček
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9cccc03bf32d472a9e75f9070a29f9e2
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spelling	oai:doaj.org-article:9cccc03bf32d472a9e75f9070a29f9e22021-12-02T15:15:58ZDecision making on vestibular schwannoma treatment: predictions based on machine-learning analysis10.1038/s41598-021-97819-x2045-2322https://doaj.org/article/9cccc03bf32d472a9e75f9070a29f9e22021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97819-xhttps://doaj.org/toc/2045-2322Abstract Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient’s preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two questions: can a single checkup predict the need of active treatment?, and which attributes of VS development are important in decision making on active treatment? Using a machine-learning analysis of medical records of 93 patients, the objectives were addressed using two classification tasks: a time-independent case-based reasoning (CBR), where each medical record was treated as independent, and a personalized dynamic analysis (PDA), during which we analyzed the individual development of each patient’s state in time. Using the CBR method we found that Koos classification of tumor size, speech reception threshold, and pure tone audiometry, collectively predict the need for active treatment with approximately 90% accuracy; in the PDA task, only the increase of Koos classification and VS size were sufficient. Our results indicate that VS treatment may be reliably predicted using only a small set of basic parameters, even without the knowledge of individual development, which may help to simplify VS treatment strategies, reduce the number of examinations, and increase cause effectiveness.Oliver ProfantZbyněk BurešZuzana BalogováJan BetkaZdeněk FíkMartin ChovanecJan VoráčekNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Oliver Profant Zbyněk Bureš Zuzana Balogová Jan Betka Zdeněk Fík Martin Chovanec Jan Voráček Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
description	Abstract Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient’s preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two questions: can a single checkup predict the need of active treatment?, and which attributes of VS development are important in decision making on active treatment? Using a machine-learning analysis of medical records of 93 patients, the objectives were addressed using two classification tasks: a time-independent case-based reasoning (CBR), where each medical record was treated as independent, and a personalized dynamic analysis (PDA), during which we analyzed the individual development of each patient’s state in time. Using the CBR method we found that Koos classification of tumor size, speech reception threshold, and pure tone audiometry, collectively predict the need for active treatment with approximately 90% accuracy; in the PDA task, only the increase of Koos classification and VS size were sufficient. Our results indicate that VS treatment may be reliably predicted using only a small set of basic parameters, even without the knowledge of individual development, which may help to simplify VS treatment strategies, reduce the number of examinations, and increase cause effectiveness.
format	article
author	Oliver Profant Zbyněk Bureš Zuzana Balogová Jan Betka Zdeněk Fík Martin Chovanec Jan Voráček
author_facet	Oliver Profant Zbyněk Bureš Zuzana Balogová Jan Betka Zdeněk Fík Martin Chovanec Jan Voráček
author_sort	Oliver Profant
title	Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
title_short	Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
title_full	Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
title_fullStr	Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
title_full_unstemmed	Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
title_sort	decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9cccc03bf32d472a9e75f9070a29f9e2
work_keys_str_mv	AT oliverprofant decisionmakingonvestibularschwannomatreatmentpredictionsbasedonmachinelearninganalysis AT zbynekbures decisionmakingonvestibularschwannomatreatmentpredictionsbasedonmachinelearninganalysis AT zuzanabalogova decisionmakingonvestibularschwannomatreatmentpredictionsbasedonmachinelearninganalysis AT janbetka decisionmakingonvestibularschwannomatreatmentpredictionsbasedonmachinelearninganalysis AT zdenekfik decisionmakingonvestibularschwannomatreatmentpredictionsbasedonmachinelearninganalysis AT martinchovanec decisionmakingonvestibularschwannomatreatmentpredictionsbasedonmachinelearninganalysis AT janvoracek decisionmakingonvestibularschwannomatreatmentpredictionsbasedonmachinelearninganalysis
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Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis

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