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

O. Profant, Z. Bureš, Z. Balogová, J. Betka, Z. Fík, M. Chovanec, J. Voráček

. 2021 ; 11 (1) : 18376. [pub] 20210915

Jazyk angličtina Země Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc22003732

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.

Citace poskytuje Crossref.org

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$a Bureš, Zbyněk $u Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Jugoslávských partyzánů 1580/3, 160 00, Prague 6, Czech Republic. zbynek.bures@cvut.cz
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$a Balogová, Zuzana $u Department of Otorhinolaryngology, 3rd Faculty of Medicine, University Hospital Královské Vinohrady, Charles University in Prague, Prague, Czech Republic
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$a Betka, Jan $u Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, University Hospital Motol, Charles University in Prague, Prague, Czech Republic
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$a Fík, Zdeněk $u Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, University Hospital Motol, Charles University in Prague, Prague, Czech Republic
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$a Chovanec, Martin $u Department of Otorhinolaryngology, 3rd Faculty of Medicine, University Hospital Královské Vinohrady, Charles University in Prague, Prague, Czech Republic
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$a Voráček, Jan $u Faculty of Management, Prague University of Economics and Business, Jindrichuv Hradec, Czech Republic
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