Cargando…
Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool
PURPOSE: Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering. MATERIAL AND METHODS: All patients were treated using radiation therapy for OPC. Dose-volume h...
Autores principales: | Wentzel, Andrew, Mohamed, Abdallah S. R., Naser, Mohamed A., van Dijk, Lisanne V., Hutcheson, Katherine, Moreno, Amy M., Fuller, Clifton D., Canahuate, Guadalupe, Marai, G. Elisabeta |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442804/ https://www.ncbi.nlm.nih.gov/pubmed/37614495 http://dx.doi.org/10.3389/fonc.2023.1210087 |
Ejemplares similares
-
Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad
por: Tardini, Elisa, et al.
Publicado: (2022) -
Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
por: Patel, Harsh, et al.
Publicado: (2021) -
Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction
por: Tosado, Joel, et al.
Publicado: (2020) -
Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer
por: van Dijk, Lisanne V., et al.
Publicado: (2023) -
Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network
por: Taku, Nicolette, et al.
Publicado: (2022)