17 November 2017 Quantification of dose uncertainties for the bladder in prostate cancer radiotherapy based on dominant eigenmodes
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Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057214 (2017) https://doi.org/10.1117/12.2283505
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia
Abstract
In radiotherapy for prostate cancer the dose at the treatment planning for the bladder may be a bad surrogate of the actual delivered dose as the bladder presents the largest inter-fraction shape variations during treatment. This paper presents PCA models as a virtual tool to estimate dosimetric uncertainties for the bladder produced by motion and deformation between fractions. Our goal is to propose a methodology to determine the minimum number of modes required to quantify dose uncertainties of the bladder for motion/deformation models based on PCA. We trained individual PCA models using the bladder contours available from three patients with a planning computed tomography (CT) and on-treatment cone-beam CTs (CBCTs). Based on the above models and via deformable image registration (DIR), we estimated two accumulated doses: firstly, an accumulated dose obtained by integrating the planning dose over the Gaussian probability distribution of the PCA model; and secondly, an accumulated dose obtained by simulating treatment courses via a Monte Carlo approach. We also computed a reference accumulated dose for each patient using his available images via DIR. Finally, we compared the planning dose with the three accumulated doses, and we calculated local dose variability and dose-volume histogram uncertainties.
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Richard Rios, Richard Rios, Oscar Acosta, Oscar Acosta, Caroline Lafond, Caroline Lafond, Jairo Espinosa, Jairo Espinosa, Renaud de Crevoisier, Renaud de Crevoisier, } "Quantification of dose uncertainties for the bladder in prostate cancer radiotherapy based on dominant eigenmodes ", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057214 (17 November 2017); doi: 10.1117/12.2283505; https://doi.org/10.1117/12.2283505
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