| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
Application of an automatic segmentation method for evaluating cardiac structure doses received by breast radiotherapy patients
|
Jung, Jae Won
|
|
|
19 |
C |
p. 138-144
|
article
|
| 2 |
A proof of concept treatment planning study of gated proton radiotherapy for cardiac soft tissue sarcoma
|
Lee, Hyeri
|
|
|
19 |
C |
p. 78-84
|
article
|
| 3 |
Automated clinical target volume delineation using deep 3D neural networks in radiation therapy of Non-small Cell Lung Cancer
|
Xie, Yunhe
|
|
|
19 |
C |
p. 131-137
|
article
|
| 4 |
Automatic 3D Monte-Carlo-based secondary dose calculation for online verification of 1.5 T magnetic resonance imaging guided radiotherapy
|
Nachbar, Marcel
|
|
|
19 |
C |
p. 6-12
|
article
|
| 5 |
Characterization of automatic treatment planning approaches in radiotherapy
|
Wortel, Geert
|
|
|
19 |
C |
p. 60-65
|
article
|
| 6 |
Clinical experience and cost evaluation of magnetic resonance imaging -only workflow in radiation therapy planning of prostate cancer
|
Keyriläinen, Jani
|
|
|
19 |
C |
p. 66-71
|
article
|
| 7 |
Comparison of atlas-based auto-segmentation accuracy for radiotherapy in prostate cancer
|
Aoyama, Takahiro
|
|
|
19 |
C |
p. 126-130
|
article
|
| 8 |
Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy
|
Thor, Maria
|
|
|
19 |
C |
p. 96-101
|
article
|
| 9 |
Feasibility of ablative stereotactic body radiation therapy of pancreas cancer patients on a 1.5 Tesla magnetic resonance-linac system using abdominal compression
|
Tyagi, Neelam
|
|
|
19 |
C |
p. 53-59
|
article
|
| 10 |
Imaging carbonic anhydrase IX as a method for monitoring hypoxia-related radioresistance in preclinical head and neck cancer models
|
Huizing, Fokko J.
|
|
|
19 |
C |
p. 145-150
|
article
|
| 11 |
Machine learning applications in radiation oncology
|
Field, Matthew
|
|
|
19 |
C |
p. 13-24
|
article
|
| 12 |
Mesorectal shape variation in rectal cancer radiotherapy in prone position using a belly board
|
Cox, Maurice C.
|
|
|
19 |
C |
p. 120-125
|
article
|
| 13 |
On-line daily plan optimization combined with a virtual couch shift procedure to address intrafraction motion in prostate magnetic resonance guided radiotherapy
|
de Muinck Keizer, Daan M.
|
|
|
19 |
C |
p. 90-95
|
article
|
| 14 |
Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning
|
Rodríguez Outeiral, Roque
|
|
|
19 |
C |
p. 39-44
|
article
|
| 15 |
Out-of-field dose in stereotactic radiotherapy for paediatric patients
|
Garrett, Lachlan
|
|
|
19 |
C |
p. 1-5
|
article
|
| 16 |
Patient position verification in magnetic-resonance imaging only radiotherapy of anal and rectal cancers
|
Bird, David
|
|
|
19 |
C |
p. 72-77
|
article
|
| 17 |
Professional practice changes in radiotherapy physics during the COVID-19 pandemic
|
Bertholet, Jenny
|
|
|
19 |
C |
p. 25-32
|
article
|
| 18 |
Prostate specific membrane antigen positron emission tomography for lesion-directed high-dose-rate brachytherapy dose escalation
|
Smith, Christopher W.
|
|
|
19 |
C |
p. 102-107
|
article
|
| 19 |
Quantification of the uncertainties within the radiotherapy dosimetry chain and their impact on tumour control
|
Bolt, Matthew
|
|
|
19 |
C |
p. 33-38
|
article
|
| 20 |
Risk of radiation-induced second malignant neoplasms from photon and proton radiotherapy in paediatric abdominal neuroblastoma
|
Taylor, Sophie
|
|
|
19 |
C |
p. 45-52
|
article
|
| 21 |
Source strength determination in iridium-192 and cobalt-60 brachytherapy: A European survey on the level of agreement between clinical measurements and manufacturer certificates
|
Vijande, Javier
|
|
|
19 |
C |
p. 108-111
|
article
|
| 22 |
The impact of image acquisition time on registration, delineation and image quality for magnetic resonance guided radiotherapy of prostate cancer patients
|
Nowee, Marlies E.
|
|
|
19 |
C |
p. 85-89
|
article
|
| 23 |
Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model
|
Lempart, Michael
|
|
|
19 |
C |
p. 112-119
|
article
|
Journal description