Cargando…
Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts
Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient’s image and perform a binary classification of the...
Autores principales: | Lombardo, Elia, Kurz, Christopher, Marschner, Sebastian, Avanzo, Michele, Gagliardi, Vito, Fanetti, Giuseppe, Franchin, Giovanni, Stancanello, Joseph, Corradini, Stefanie, Niyazi, Maximilian, Belka, Claus, Parodi, Katia, Riboldi, Marco, Landry, Guillaume |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979766/ https://www.ncbi.nlm.nih.gov/pubmed/33742070 http://dx.doi.org/10.1038/s41598-021-85671-y |
Ejemplares similares
-
Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy
por: Marschner, Sebastian N., et al.
Publicado: (2021) -
Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors
por: Ribeiro, Marvin F., et al.
Publicado: (2023) -
ScatterNet for projection-based 4D cone-beam computed tomography intensity correction of lung cancer patients
por: Schmitz, Henning, et al.
Publicado: (2023) -
Assessment of intrafractional prostate motion and its dosimetric impact in MRI-guided online adaptive radiotherapy with gating
por: Xiong, Yuqing, et al.
Publicado: (2022) -
MR-guided radiotherapy in node-positive non-small cell lung cancer and severely limited pulmonary reserve: a report proposing a new clinical pathway for the management of high-risk patients
por: Eze, Chukwuka, et al.
Publicado: (2022)