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...
Guardado en:
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: | |
Acceso en línea: | https://doaj.org/article/9cccc03bf32d472a9e75f9070a29f9e2 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
por: Dixiang Song, et al.
Publicado: (2021) -
Sudden sensorineural hearing loss in patients with vestibular schwannoma
por: Koichiro Wasano, et al.
Publicado: (2021) -
Tumor-Associated Macrophages in Vestibular Schwannoma and Relationship to Hearing
por: Eric Nisenbaum MD, et al.
Publicado: (2021) -
Optimized preoperative determination of nerve of origin in patients with vestibular schwannoma
por: Torsten Rahne, et al.
Publicado: (2021) -
Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma
por: Jessica E. Sagers, et al.
Publicado: (2018)