| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Acknowledgement of Reviewers 2020
|
|
|
|
153 |
C |
p. I
|
artikel
|
| 2 |
ADC measurements on the Unity MR-linac – A recommendation on behalf of the Elekta Unity MR-linac consortium
|
Kooreman, Ernst S.
|
|
|
153 |
C |
p. 106-113
|
artikel
|
| 3 |
A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases
|
Cusumano, Davide
|
|
|
153 |
C |
p. 205-212
|
artikel
|
| 4 |
A multi-institutional assessment of COVID-19-related risk in radiation oncology
|
Viscariello, Natalie
|
|
|
153 |
C |
p. 296-302
|
artikel
|
| 5 |
Analysis of cardiac substructure dose in a large, multi-centre danish breast cancer cohort (the DBCG HYPO trial): Trends and predictive modelling
|
Finnegan, Robert
|
|
|
153 |
C |
p. 130-138
|
artikel
|
| 6 |
A physicochemical model of reaction kinetics supports peroxyl radical recombination as the main determinant of the FLASH effect
|
Labarbe, Rudi
|
|
|
153 |
C |
p. 303-310
|
artikel
|
| 7 |
Beam data modeling of linear accelerators (linacs) through machine learning and its potential applications in fast and robust linac commissioning and quality assurance
|
Zhao, Wei
|
|
|
153 |
C |
p. 122-129
|
artikel
|
| 8 |
Beginnings, endings, histories and horizons
|
Thwaites, David
|
|
|
153 |
C |
p. 1-4
|
artikel
|
| 9 |
Clarifications on our review on estimating dose delivery accuracy in stereotactic body radiation therapy: A review of in-vivo measurement methods: In response to the letter of Kos
|
Esposito, Marco
|
|
|
153 |
C |
p. 320-321
|
artikel
|
| 10 |
Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer
|
Choi, Min Seo
|
|
|
153 |
C |
p. 139-145
|
artikel
|
| 11 |
Clinical paradigms and challenges in surface guided radiation therapy: Where do we go from here?
|
Batista, Vania
|
|
|
153 |
C |
p. 34-42
|
artikel
|
| 12 |
Contents
|
|
|
|
153 |
C |
p. iii-iv
|
artikel
|
| 13 |
Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy
|
Maspero, Matteo
|
|
|
153 |
C |
p. 197-204
|
artikel
|
| 14 |
Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours
|
Florkow, Mateusz C.
|
|
|
153 |
C |
p. 220-227
|
artikel
|
| 15 |
Deep learning for elective neck delineation: More consistent and time efficient
|
van der Veen, J.
|
|
|
153 |
C |
p. 180-188
|
artikel
|
| 16 |
Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy
|
Liu, Zhikai
|
|
|
153 |
C |
p. 172-179
|
artikel
|
| 17 |
Development and validation of an age-scalable cardiac model with substructures for dosimetry in late-effects studies of childhood cancer survivors
|
Shrestha, Suman
|
|
|
153 |
C |
p. 163-171
|
artikel
|
| 18 |
Dose-averaged linear energy transfer per se does not correlate with late rectal complications in carbon-ion radiotherapy
|
Okonogi, Noriyuki
|
|
|
153 |
C |
p. 272-278
|
artikel
|
| 19 |
Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices
|
Kandalan, Roya Norouzi
|
|
|
153 |
C |
p. 228-235
|
artikel
|
| 20 |
Dose-response relationships for radiation-related heart disease: Impact of uncertainties in cardiac dose reconstruction
|
Ntentas, Georgios
|
|
|
153 |
C |
p. 155-162
|
artikel
|
| 21 |
Editorial Board
|
|
|
|
153 |
C |
p. ii
|
artikel
|
| 22 |
From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community
|
Kazmierska, Joanna
|
|
|
153 |
C |
p. 43-54
|
artikel
|
| 23 |
Grand challenges for medical physics in radiation oncology
|
Fiorino, Claudio
|
|
|
153 |
C |
p. 7-14
|
artikel
|
| 24 |
Identification of treatment error types for lung cancer patients using convolutional neural networks and EPID dosimetry
|
Wolfs, Cecile J.A.
|
|
|
153 |
C |
p. 243-249
|
artikel
|
| 25 |
Improvement of prediction and classification performance for gamma passing rate by using plan complexity and dosiomics features
|
Hirashima, Hideaki
|
|
|
153 |
C |
p. 250-257
|
artikel
|
| 26 |
Letter to the editor of radiotherapy and oncology regarding the article esposito et al: Estimating dose delivery accuracy in stereotactic body radiation therapy: A review of in-vivo measurement methods
|
Kos, Sandra
|
|
|
153 |
C |
p. 319
|
artikel
|
| 27 |
Low dose radiation therapy for COVID-19: Effective dose and estimation of cancer risk
|
García-Hernández, Trinitat
|
|
|
153 |
C |
p. 289-295
|
artikel
|
| 28 |
MetNet: Computer-aided segmentation of brain metastases in post-contrast T1-weighted magnetic resonance imaging
|
Zhou, Zijian
|
|
|
153 |
C |
p. 189-196
|
artikel
|
| 29 |
Multivariate normal tissue complication probability models for rectal and bladder morbidity in prostate cancer patients treated with proton therapy
|
Pedersen, Jesper
|
|
|
153 |
C |
p. 279-288
|
artikel
|
| 30 |
Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures
|
Sanduleanu, Sebastian
|
|
|
153 |
C |
p. 97-105
|
artikel
|
| 31 |
Optimization and auto-segmentation of a high risk cardiac zone for heart sparing in breast cancer radiotherapy
|
Loap, Pierre
|
|
|
153 |
C |
p. 146-154
|
artikel
|
| 32 |
Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance
|
Vandewinckele, Liesbeth
|
|
|
153 |
C |
p. 55-66
|
artikel
|
| 33 |
Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part I: Intra-fraction breathing motion management
|
Anastasi, Gail
|
|
|
153 |
C |
p. 79-87
|
artikel
|
| 34 |
Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part II: Offline and online plan adaption for interfractional changes
|
Bertholet, Jenny
|
|
|
153 |
C |
p. 88-96
|
artikel
|
| 35 |
Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer
|
van Houdt, Petra J.
|
|
|
153 |
C |
p. 114-121
|
artikel
|
| 36 |
Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies
|
Hansen, Christian Rønn
|
|
|
153 |
C |
p. 67-78
|
artikel
|
| 37 |
RealDRR – Rendering of realistic digitally reconstructed radiographs using locally trained image-to-image translation
|
Dhont, Jennifer
|
|
|
153 |
C |
p. 213-219
|
artikel
|
| 38 |
Surface guided motion management in glottic larynx stereotactic body radiation therapy
|
Zhao, Bo
|
|
|
153 |
C |
p. 236-242
|
artikel
|
| 39 |
The gimbaled-head radiotherapy system: Rise and downfall of a dedicated system for dynamic tumor tracking with real-time monitoring and dynamic WaveArc
|
Hiraoka, Masahiro
|
|
|
153 |
C |
p. 311-318
|
artikel
|
| 40 |
The relative biological effectiveness of carbon ion radiation therapy for early stage lung cancer
|
Jeong, Jeho
|
|
|
153 |
C |
p. 265-271
|
artikel
|
| 41 |
The role of computational methods for automating and improving clinical target volume definition
|
Unkelbach, Jan
|
|
|
153 |
C |
p. 15-25
|
artikel
|
| 42 |
Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer
|
Mori, Martina
|
|
|
153 |
C |
p. 258-264
|
artikel
|
| 43 |
Tribute to David Thwaites
|
Georg, Dietmar
|
|
|
153 |
C |
p. 5-6
|
artikel
|
| 44 |
What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans
|
Hernandez, Victor
|
|
|
153 |
C |
p. 26-33
|
artikel
|
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