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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68378041:_____/21:00551108 RIV/00216208:11110/21:10431688 RIV/61384399:31160/21:00056876 RIV/68407700:21460/21:00353364 a 3 dalších

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient&apos;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&apos;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.

Název v anglickém jazyce

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

Popis výsledku anglicky

Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient&apos;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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30206 - Otorhinolaryngology

Projekt

<a href="/cs/project/GA19-08241S" target="_blank" >GA19-08241S: Změny ve sluchové kůře u pacientů s jednostrannou hluchotou</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Svazek periodika

11

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

18376

Kód UT WoS článku

000696347000073

EID výsledku v databázi Scopus

2-s2.0-85115245768