scholarly journals Xerostomia Impacts Dysgeusia in Oropharyngeal Cancer Patients Treated with Proton Therapy

2019 ◽  
Vol 105 (1) ◽  
pp. E414-E415
Author(s):  
T.S. Deshpande ◽  
G.B. Gunn ◽  
C.D. Fuller ◽  
R. Ye ◽  
D.I. Rosenthal ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Proton Therapy ◽  
Oropharyngeal Cancer

scholarly journals OC-0154: Proton therapy patient selection for oropharyngeal cancer patients: the impact of treatment accuracy

2017 ◽  
Vol 123 ◽  
pp. S77-S78 ◽  
Author(s):  
M. Hoogeman ◽  
S. Breedveld ◽  
M. De Jong ◽  
E. Astreinidou ◽  
L. Tans ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Patient Selection ◽  
Proton Therapy ◽  
Oropharyngeal Cancer ◽  
Selection For ◽  
The Impact

scholarly journals Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

2017 ◽  
Vol 56 (11) ◽  
pp. 1444-1450 ◽  
Author(s):  
Rik G. Bijman ◽  
Sebastiaan Breedveld ◽  
Tine Arts ◽  
Eleftheria Astreinidou ◽  
Martin A. de Jong ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Proton Therapy ◽  
Oropharyngeal Cancer ◽  
Model Based ◽  
Dose Uncertainty ◽  
Selection Of

scholarly journals The impact of treatment accuracy on proton therapy patient selection for oropharyngeal cancer patients

2017 ◽  
Vol 125 (3) ◽  
pp. 520-525 ◽  
Author(s):  
Tine Arts ◽  
Sebastiaan Breedveld ◽  
Martin A. de Jong ◽  
Eleftheria Astreinidou ◽  
Lisa Tans ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Patient Selection ◽  
Proton Therapy ◽  
Oropharyngeal Cancer ◽  
Selection For ◽  
The Impact

scholarly journals Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 786
Author(s):  
Daniel M. Lang ◽  
Jan C. Peeken ◽  
Stephanie E. Combs ◽  
Jan J. Wilkens ◽  
Stefan Bartzsch
Keyword(s):  
Deep Learning ◽  
Cancer Patients ◽  
Oropharyngeal Cancer ◽  
Characteristic Curve ◽  
Learning Models ◽  
Convolutional Network ◽  
Hpv Status ◽  
3D Information ◽  
External Test

Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.

scholarly journals Preselection Tool for the Model-Based Selection of Head and Neck Cancer Patients for Proton Therapy

2019 ◽  
Vol 105 (1) ◽  
pp. E384
Author(s):  
M. Tambas ◽  
H.P. van der Laan ◽  
A.V.D. Hoek ◽  
H.P. Bijl ◽  
M. Dieters ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Proton Therapy ◽  
Model Based ◽  
Selection Of
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