Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064203%3A_____%2F21%3A10431688" target="_blank" >RIV/00064203:_____/21:10431688 - isvavai.cz</a>
Alternative codes found
RIV/68378041:_____/21:00551108 RIV/00216208:11110/21:10431688 RIV/61384399:31160/21:00056876 RIV/68407700:21460/21:00353364 and 3 more
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=SVKi0lhr._" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=SVKi0lhr._</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-021-97819-x" target="_blank" >10.1038/s41598-021-97819-x</a>
Alternative languages
Result language
angličtina
Original language name
Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30206 - Otorhinolaryngology
Result continuities
Project
<a href="/en/project/GA19-08241S" target="_blank" >GA19-08241S: Changes in the auditory cortex in patients with single sided deafness</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Reports
ISSN
2045-2322
e-ISSN
2045-2322
Volume of the periodical
11
Issue of the periodical within the volume
September
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
18376
UT code for WoS article
000696347000073
EID of the result in the Scopus database
2-s2.0-85115245768