Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist
Background and PurposeMachine learning (ML) is emerging as a feasible approach to optimize patients’ care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented s...
Guardado en:
Autores principales: | Stefania Volpe, Matteo Pepa, Mattia Zaffaroni, Federica Bellerba, Riccardo Santamaria, Giulia Marvaso, Lars Johannes Isaksson, Sara Gandini, Anna Starzyńska, Maria Cristina Leonardi, Roberto Orecchia, Daniela Alterio, Barbara Alicja Jereczek-Fossa |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/38f4a301a277435aba628cbb701dbb26 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Mixed-Beam Approach for High-Risk Prostate Cancer Carbon-Ion Boost Followed by Photon Intensity-Modulated Radiotherapy: Preliminary Results of Phase II Trial AIRC-IG-14300
por: Giulia Marvaso, et al.
Publicado: (2021) -
Voxel-based analysis unveils regional dose differences associated with radiation-induced morbidity in head and neck cancer patients
por: Serena Monti, et al.
Publicado: (2017) -
Corrigendum: A Real-World, Multicenter, Observational Retrospective Study of Durvalumab After Concomitant or Sequential Chemoradiation for Unresectable Stage III Non-Small Cell Lung Cancer
por: Alessio Bruni, et al.
Publicado: (2021) -
A Real-World, Multicenter, Observational Retrospective Study of Durvalumab After Concomitant or Sequential Chemoradiation for Unresectable Stage III Non-Small Cell Lung Cancer
por: Alessio Bruni, et al.
Publicado: (2021) -
Community oncologists’ perceptions and utilization of large-panel genomic tumor testing
por: Eric C. Anderson, et al.
Publicado: (2021)