» Authors » Rik Bijman

Rik Bijman

Explore the profile of Rik Bijman including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 6
Citations 74
Followers 0
Related Specialties
Top 10 Co-Authors
Published In
Affiliations
Soon will be listed here.
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.
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...