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Dosimetric assessment of prostate cancer patients through principal component analysis (PCA)

The aims of this study were twofold: first, to determine the impact of variance in dose‐volume histograms (DVH) on patient‐specific toxicity after 2 high‐dose fractions in a sample of 22 men with prostate cancer; and second, to compare the effectiveness of traditional DVH analysis and principal comp...

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Autores principales: Gloi, Aime M, Buchanan, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713663/
https://www.ncbi.nlm.nih.gov/pubmed/23318379
http://dx.doi.org/10.1120/jacmp.v14i1.3882
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author Gloi, Aime M
Buchanan, Robert
author_facet Gloi, Aime M
Buchanan, Robert
author_sort Gloi, Aime M
collection PubMed
description The aims of this study were twofold: first, to determine the impact of variance in dose‐volume histograms (DVH) on patient‐specific toxicity after 2 high‐dose fractions in a sample of 22 men with prostate cancer; and second, to compare the effectiveness of traditional DVH analysis and principal component analysis (PCA) in predicting rectum and urethra toxicity. A series of 22 patients diagnosed with prostate adenocarcinoma was treated with 45 Gy external beam and 20 Gy dose rate brachytherapy. Principal component analysis was applied to model the shapes of the rectum and urethra dose‐volume histograms. We used logistic regression to measure the correlations between the principal components and the incidence of rectal bleeding and urethra stricture. We also calculated the equivalent uniform dose (EUD) and normal tissue complication probability (NTCP) for the urethra and rectum, and tumor control probability (TCP) for the prostate using BioSuite software. We evaluated their correlations with rectal and urethra toxicity. The rectum DVHs are well described by one principal component (PC1), which accounts for 93.5% of the variance in their shapes. The urethra DVHs are described by two principal components, PC1 and PC2, which account for 94.98% and 3.15% of the variance, respectively. Multivariate exact logistic regression suggests that urethra PC2 is a good predictor of stricture, with Nagelkerke's [Formula: see text] estimated at 0.798 and a Wald criterion of 5.421 ([Formula: see text]). The average NTCPs were [Formula: see text] and [Formula: see text] for the rectum and urethra, respectively. The average TCP was [Formula: see text]. This study suggests that principal component analysis can be used to identify the shape variation in dose‐volume histograms, and that the principal components can be correlated with the toxicity of a treatment plan based on multivariate analysis. The principal components are also correlated with traditional dosimetric parameters. PACS number: 3.6.96.0
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spelling pubmed-57136632018-04-02 Dosimetric assessment of prostate cancer patients through principal component analysis (PCA) Gloi, Aime M Buchanan, Robert J Appl Clin Med Phys Radiation Oncology Physics The aims of this study were twofold: first, to determine the impact of variance in dose‐volume histograms (DVH) on patient‐specific toxicity after 2 high‐dose fractions in a sample of 22 men with prostate cancer; and second, to compare the effectiveness of traditional DVH analysis and principal component analysis (PCA) in predicting rectum and urethra toxicity. A series of 22 patients diagnosed with prostate adenocarcinoma was treated with 45 Gy external beam and 20 Gy dose rate brachytherapy. Principal component analysis was applied to model the shapes of the rectum and urethra dose‐volume histograms. We used logistic regression to measure the correlations between the principal components and the incidence of rectal bleeding and urethra stricture. We also calculated the equivalent uniform dose (EUD) and normal tissue complication probability (NTCP) for the urethra and rectum, and tumor control probability (TCP) for the prostate using BioSuite software. We evaluated their correlations with rectal and urethra toxicity. The rectum DVHs are well described by one principal component (PC1), which accounts for 93.5% of the variance in their shapes. The urethra DVHs are described by two principal components, PC1 and PC2, which account for 94.98% and 3.15% of the variance, respectively. Multivariate exact logistic regression suggests that urethra PC2 is a good predictor of stricture, with Nagelkerke's [Formula: see text] estimated at 0.798 and a Wald criterion of 5.421 ([Formula: see text]). The average NTCPs were [Formula: see text] and [Formula: see text] for the rectum and urethra, respectively. The average TCP was [Formula: see text]. This study suggests that principal component analysis can be used to identify the shape variation in dose‐volume histograms, and that the principal components can be correlated with the toxicity of a treatment plan based on multivariate analysis. The principal components are also correlated with traditional dosimetric parameters. PACS number: 3.6.96.0 John Wiley and Sons Inc. 2013-01-07 /pmc/articles/PMC5713663/ /pubmed/23318379 http://dx.doi.org/10.1120/jacmp.v14i1.3882 Text en © 2013 The Authors. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Gloi, Aime M
Buchanan, Robert
Dosimetric assessment of prostate cancer patients through principal component analysis (PCA)
title Dosimetric assessment of prostate cancer patients through principal component analysis (PCA)
title_full Dosimetric assessment of prostate cancer patients through principal component analysis (PCA)
title_fullStr Dosimetric assessment of prostate cancer patients through principal component analysis (PCA)
title_full_unstemmed Dosimetric assessment of prostate cancer patients through principal component analysis (PCA)
title_short Dosimetric assessment of prostate cancer patients through principal component analysis (PCA)
title_sort dosimetric assessment of prostate cancer patients through principal component analysis (pca)
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713663/
https://www.ncbi.nlm.nih.gov/pubmed/23318379
http://dx.doi.org/10.1120/jacmp.v14i1.3882
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