Rik Bijman
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Explore the profile of Rik Bijman including associated specialties, affiliations and a list of published articles.
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6
Citations
74
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Recent Articles
1.
Leitao J, Bijman R, Sharfo A, Brus Y, Rossi L, Breedveld S, et al.
Phys Med
. 2022 Jul;
101:20-27.
PMID: 35853387
Purpose: Complexity in selecting optimal non-coplanar beam setups and prolonged delivery times may hamper the use of non-coplanar treatments for nasopharyngeal carcinoma (NPC). Automated multi-criterial planning with integrated beam angle...
2.
Bijman R, Rossi L, Janssen T, de Ruiter P, van Triest B, Breedveld S, et al.
Front Oncol
. 2021 Oct;
11:717681.
PMID: 34660281
Background: With the large-scale introduction of volumetric modulated arc therapy (VMAT), selection of optimal beam angles for coplanar static-beam IMRT has increasingly become obsolete. Due to unavailability of VMAT in...
3.
Bijman R, Sharfo A, Rossi L, Breedveld S, Heijmen B
Radiother Oncol
. 2021 Mar;
158:253-261.
PMID: 33711413
Introduction: Many approaches for automated treatment planning (autoplanning) have been proposed and investigated. Autoplanning can enhance plan quality compared to 'manual' trial-and-error planning, and decrease routine planning workload. A few...
4.
Bijman R, Rossi L, Sharfo A, Heemsbergen W, Incrocci L, Breedveld S, et al.
Front Oncol
. 2020 Jul;
10:943.
PMID: 32695670
Currently, radiation-oncologists generally evaluate a single treatment plan for each patient that is possibly adapted by the planner prior to final approval. There is no systematic exploration of patient-specific trade-offs...
5.
Bijman R, Rossi L, Janssen T, de Ruiter P, Carbaat C, van Triest B, et al.
Acta Oncol
. 2020 May;
59(8):926-932.
PMID: 32436450
In this study we developed a workflow for fully-automated generation of deliverable IMRT plans for a 1.5 T MR-Linac (MRL) based on contoured CT scans, and we evaluated automated MRL...
6.
Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy
Rossi L, Bijman R, Schillemans W, Aluwini S, Cavedon C, Witte M, et al.
Radiother Oncol
. 2018 Sep;
129(3):548-553.
PMID: 30177372
Background And Purpose: To explore the use of texture analysis (TA) features of patients' 3D dose distributions to improve prediction modelling of treatment complication rates in prostate cancer radiotherapy. Material...