SU-E-T-645: Radiobiologic Evaluation of Lung Toxicity in Treatment Plan Quality Comparisons for Hypofractionated Stereotactic Body Radiotherapy Using Relative Damaged Volume Modeling

2011 ◽  
Vol 38 (6Part20) ◽  
pp. 3638-3638
Author(s):  
C Han ◽  
K Nguyen ◽  
J Kim ◽  
R Pezner ◽  
T Schultheiss ◽  
...  
Keyword(s):  
Stereotactic Body Radiotherapy ◽  
Treatment Plan ◽  
Lung Toxicity ◽  
Plan Quality ◽  
Volume Modeling

Treatment plan quality and delivery accuracy assessments on 3 IMRT delivery methods of stereotactic body radiotherapy for spine tumors

2019 ◽  
Vol 44 (1) ◽  
pp. 11-14 ◽  
Author(s):  
Long Huang ◽  
Toufik Djemil ◽  
Tingliang Zhuang ◽  
Martin Andrews ◽  
Samuel T. Chao ◽  
...  
Keyword(s):  
Stereotactic Body Radiotherapy ◽  
Treatment Plan ◽  
Plan Quality ◽  
Delivery Methods ◽  
Spine Tumors ◽  
Delivery Accuracy ◽  
Accuracy Assessments

scholarly journals Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Stefan Gerlach ◽  
Christoph Fürweger ◽  
Theresa Hofmann ◽  
Alexander Schlaefer
Keyword(s):  
Deep Learning ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Plan Quality ◽  
New Approach ◽  
Beam Generation ◽  
Radiological Features ◽  
Improve Treatment ◽  
Robotic Radiosurgery ◽  
Treatment Plans

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.

PO-1435: Large treatment plan quality enhancement in robotic radiotherapy

2020 ◽  
Vol 152 ◽  
pp. S762-S763
Author(s):  
M. Giżyńska ◽  
L. Rossi ◽  
W. Den Toom ◽  
M. Milder ◽  
L. Inrocci ◽  
...  
Keyword(s):  
Treatment Plan ◽  
Quality Enhancement ◽  
Plan Quality

SP-0452 Treatment plan quality assessment

2021 ◽  
Vol 161 ◽  
pp. S348-S349
Author(s):  
V. Hernandez ◽  
C. Rønn Hansen ◽  
L. Widesott ◽  
N. Jornet
Keyword(s):  
Quality Assessment ◽  
Treatment Plan ◽  
Plan Quality

scholarly journals A novel index for assessing treatment plan quality in stereotactic radiosurgery

2018 ◽  
Vol 129 (Suppl1) ◽  
pp. 118-124 ◽  
Author(s):  
Alexis Dimitriadis ◽  
Ian Paddick
Keyword(s):  
Stereotactic Radiosurgery ◽  
Treatment Plan ◽  
Efficiency Index ◽  
Target Volume ◽  
Surrounding Tissue ◽  
Plan Quality ◽  
Multiple Target ◽  
Integral Dose ◽  
Plan Optimization ◽  
Dose Volume

OBJECTIVEStereotactic radiosurgery (SRS) is characterized by high levels of conformity and steep dose gradients from the periphery of the target to surrounding tissue. Clinical studies have backed up the importance of these factors through evidence of symptomatic complications. Available data suggest that there are threshold doses above which the risk of symptomatic radionecrosis increases with the volume irradiated. Therefore, radiosurgical treatment plans should be optimized by minimizing dose to the surrounding tissue while maximizing dose to the target volume. Several metrics have been proposed to quantify radiosurgical plan quality, but all present certain weaknesses. To overcome limitations of the currently used metrics, a novel metric is proposed, the efficiency index (η50%), which is based on the principle of calculating integral doses: η50% = integral doseTV/integral dosePIV50%.METHODSThe value of η50% can be easily calculated by dividing the integral dose (mean dose × volume) to the target volume (TV) by the integral dose to the volume of 50% of the prescription isodose (PIV50%). Alternatively, differential dose-volume histograms (DVHs) of the TV and PIV50% can be used. The resulting η50% value is effectively the proportion of energy within the PIV50% that falls into the target. This value has theoretical limits of 0 and 1, with 1 being perfect. The index combines conformity, gradient, and mean dose to the target into a single value. The value of η50% was retrospectively calculated for 100 clinical SRS plans.RESULTSThe value of η50% for the 100 clinical SRS plans ranged from 37.7% to 58.0% with a mean value of 49.0%. This study also showed that the same principles used for the calculation of η50% can be adapted to produce an index suitable for multiple-target plans (Gη12Gy). Furthermore, the authors present another adaptation of the index that may play a role in plan optimization by calculating and minimizing the proportion of energy delivered to surrounding organs at risk (OARη50%).CONCLUSIONSThe proposed efficiency index is a novel approach in quantifying plan quality by combining conformity, gradient, and mean dose into a single value. It quantifies the ratio of the dose “doing good” versus the dose “doing harm,” and its adaptations can be used for multiple-target plan optimization and OAR sparing.

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