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

0551108 - ÚEM 2022 RIV GB eng J - Journal Article
Profant, Oliver - Bureš, Zbyněk - Balogová, Zuzana - Betka, J. - Fík, Z. - Chovanec, M. - Voráček, J.
Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis.
Scientific Reports. Roč. 11, č. 1 (2021), č. článku 18376. ISSN 2045-2322. E-ISSN 2045-2322
R&D Projects: GA ČR(CZ) GA19-08241S
Institutional support: RVO:68378041
Keywords : conservative management * growth * experience * diagnosis
OECD category: Otorhinolaryngology
Impact factor: 4.997, year: 2021
Method of publishing: Open access
https://www.nature.com/articles/s41598-021-97819-x

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.
Permanent Link: http://hdl.handle.net/11104/0326509