scholarly journals Variations in Head and Neck Treatment Plan Quality Assessment Among Radiation Oncologists and Medical Physicists in a Single Radiotherapy Department

2021 ◽  
Vol 11 ◽  
Author(s):  
Elisabetta Cagni ◽  
Andrea Botti ◽  
Linda Rossi ◽  
Cinzia Iotti ◽  
Mauro Iori ◽  
...  

BackgroundAgreement between planners and treating radiation oncologists (ROs) on plan quality criteria is essential for consistent planning. Differences between ROs and planning medical physicists (MPs) in perceived quality of head and neck cancer plans were assessed.Materials and MethodsFive ROs and four MPs scored 65 plans for in total 15 patients. For each patient, the clinical (CLIN) plan and two or four alternative plans, generated with automated multi-criteria optimization (MCO), were included. There was always one MCO plan aiming at maximally adhering to clinical plan requirements, while the other MCO plans had a lower aimed quality. Scores were given as follows: 1–7 and 1–2, not acceptable; 3–5, acceptable if further planning would not resolve perceived weaknesses; and 6–7, straightway acceptable. One MP and one RO repeated plan scoring for intra-observer variation assessment.ResultsFor the 36 unique observer pairs, the median percentage of plans for which the two observers agreed on a plan score (100% = 65 plans) was 27.7% [6.2, 40.0]. In the repeat scoring, agreements between first and second scoring were 52.3% and 40.0%, respectively. With a binary division between unacceptable (scores 1 and 2) and acceptable (3–7) plans, the median inter-observer agreement percentage was 78.5% [63.1, 86.2], while intra-observer agreements were 96.9% and 86.2%. There were no differences in observed agreements between RO–RO, MP–MP, and RO–MP pairs. Agreements for the highest-quality, automatically generated MCO plans were higher than for the CLIN plans.ConclusionsInter-observer differences in plan quality scores were substantial and could result in inconsistencies in generated treatment plans. Agreements among ROs were not better than between ROs and MPs, despite large differences in training and clinical role. High-quality automatically generated plans showed the best score agreements.

2016 ◽  
Vol 58 (4) ◽  
pp. 579-590 ◽  
Author(s):  
Ghulam Murtaza ◽  
Stefania Cora ◽  
Ehsan Ullah Khan

Abstract Volumetric-modulated arc therapy (VMAT) is an efficient form of radiotherapy used to deliver intensity-modulated radiotherapy beams. The aim of this study was to investigate the relative insensitivity of VMAT plan quality to gantry angle spacing (GS). Most previous VMAT planning and dosimetric work for GS resolution has been conducted for single arc VMAT. In this work, a quantitative comparison of dose–volume indices (DIs) was made for partial-, single- and double-arc VMAT plans optimized at 2°, 3° and 4° GS, representing a large variation in deliverable multileaf collimator segments. VMAT plans of six prostate cancer and six head-and-neck cancer patients were simulated for an Elekta SynergyS® Linac (Elekta Ltd, Crawley, UK), using the SmartArc™ module of Pinnacle³ TPS, (version 9.2, Philips Healthcare). All optimization techniques generated clinically acceptable VMAT plans, except for the single-arc for the head-and-neck cancer patients. Plan quality was assessed by comparing the DIs for the planning target volume, organs at risk and normal tissue. A GS of 2°, with finest resolution and consequently highest intensity modulation, was considered to be the reference, and this was compared with GS 3° and 4°. The differences between the majority of reference DIs and compared DIs were <2%. The metrics, such as treatment plan optimization time and pretreatment (phantom) dosimetric calculation time, supported the use of a GS of 4°. The ArcCHECK™ phantom–measured dosimetric agreement verifications resulted in a >95.0% passing rate, using the criteria for γ (3%, 3 mm). In conclusion, a GS of 4° is an optimal choice for minimal usage of planning resources without compromise of plan quality.


2015 ◽  
Vol 115 ◽  
pp. S463-S464
Author(s):  
A. Kyroudi ◽  
F. Bochud ◽  
K. Petersson ◽  
M. Ozsahin ◽  
J. Bourhis ◽  
...  

2017 ◽  
Vol 72 ◽  
pp. S102 ◽  
Author(s):  
W. Verbakel ◽  
N. Raaijmakers ◽  
L. Bos ◽  
M. Essers ◽  
C. Terhaard ◽  
...  

2012 ◽  
Vol 39 (5) ◽  
pp. 2708-2712 ◽  
Author(s):  
D. Ruan ◽  
W. Shao ◽  
J. DeMarco ◽  
S. Tenn ◽  
C. King ◽  
...  

Author(s):  
Diyana Afrina Hizam ◽  
Wei Loong Jong ◽  
Hafiz Mohd Zin ◽  
Kwan Hoong Ng ◽  
Ngie Min Ung

2016 ◽  
Vol 119 (2) ◽  
pp. 337-343 ◽  
Author(s):  
Jim P. Tol ◽  
Patricia Doornaert ◽  
Birgit I. Witte ◽  
Max Dahele ◽  
Ben J. Slotman ◽  
...  

2016 ◽  
Vol 192 (8) ◽  
pp. 507-515 ◽  
Author(s):  
David Krug ◽  
Rene Baumann ◽  
Thorsten Rieckmann ◽  
Emmanouil Fokas ◽  
Tobias Gauer ◽  
...  

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Stefan Gerlach ◽  
Christoph Fürweger ◽  
Theresa Hofmann ◽  
Alexander Schlaefer

AbstractAlthough robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Furthermore, different clinical goals have to be considered during planning and generally different sets of beams correspond to different clinical goals. Typically, candidate beams sampled from a randomized heuristic form the basis for treatment planning. We propose a new approach to generate candidate beams based on deep learning using radiological features as well as the desired constraints. We demonstrate that candidate beams generated for specific clinical goals can improve treatment plan quality. Furthermore, we compare two approaches to include information about constraints in the prediction. Our results show that CNN generated beams can improve treatment plan quality for different clinical goals, increasing coverage from 91.2 to 96.8% for 3,000 candidate beams on average. When including the clinical goal in the training, coverage is improved by 1.1% points.


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