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Verfasst von:Klement, Rainer J. [VerfasserIn]   i
 Sonke, Jan-Jakob [VerfasserIn]   i
 Allgaeuer, Michael [VerfasserIn]   i
 Andratschke, Nicolaus [VerfasserIn]   i
 Appold, Steffen [VerfasserIn]   i
 Belderbos, Jose [VerfasserIn]   i
 Belka, Claus [VerfasserIn]   i
 Blanck, Oliver [VerfasserIn]   i
 Dieckmann, Karin [VerfasserIn]   i
 Eich, Hans T. [VerfasserIn]   i
 Mantel, Frederick [VerfasserIn]   i
 Eble, Michael [VerfasserIn]   i
 Hope, Andrew [VerfasserIn]   i
 Grosu, Anca L. [VerfasserIn]   i
 Nevinny-Stickel, Meinhard [VerfasserIn]   i
 Semrau, Sabine [VerfasserIn]   i
 Sweeney, Reinhart A. [VerfasserIn]   i
 Hörner-Rieber, Juliane [VerfasserIn]   i
 Werner-Wasik, Maria [VerfasserIn]   i
 Engenhart-Cabillic, Rita [VerfasserIn]   i
 Ye, Hong [VerfasserIn]   i
 Grills, Inga [VerfasserIn]   i
 Guckenberger, Matthias [VerfasserIn]   i
Titel:Correlating dose variables with local tumor control in stereotactic body radiation therapy for early-stage non-small cell lung cancer
Titelzusatz:a modeling study on 1500 individual treatments
Verf.angabe:Rainer J. Klement, PhD, Jan-Jakob Sonke, PhD, Michael Allgaeuer, MD, Nicolaus Andratschke, MD, Steffen Appold, MD, Jose Belderbos, MD, PhD, Claus Belka, MD, Oliver Blanck, PhD, Karin Dieckmann, MD, Hans T. Eich, MD, Frederick Mantel, MD, Michael Eble, MD, Andrew Hope, MD, Anca L. Grosu, MD, Meinhard Nevinny-Stickel, MD, Sabine Semrau, MD, Reinhart A. Sweeney, MD, Juliane Horner-Rieber, MD, Maria Werner-Wasik, MD, Rita Engenhart-Cabillic, MD, Hong Ye, PhD, Inga Grills, MD, Matthias Guckenberger, MD
E-Jahr:2020
Jahr:15 March 2020
Umfang:8 S.
Fussnoten:Gesehen am 09.02.2021
Titel Quelle:Enthalten in: International journal of radiation oncology, biology, physics
Ort Quelle:Amsterdam [u.a.] : Elsevier Science, 1975
Jahr Quelle:2020
Band/Heft Quelle:107(2020), 3, Seite 579-586
ISSN Quelle:1879-355X
Abstract:Background: Large variation regarding prescription and dose inhomogeneity exists in stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer. The aim of this modeling study was to identify which dose metric correlates best with local tumor control probability to make recommendations regarding SBRT prescription. Methods and Materials: We combined 2 retrospective databases of patients with non-small cell lung cancer, yielding 1500 SBRT treatments for analysis. Three dose parameters were converted to biologically effective doses (BEDs): (1) the (near-minimum) dose prescribed to the planning target volume (PTV) periphery (yielding BEDmin); (2) the (near-maximum) dose absorbed by 1% of the PTV (yielding BEDmax); and (3) the average between near-minimum and near-maximum doses (yielding BEDave). These BED parameters were then correlated to the risk of local recurrence through Cox regression. Furthermore, BED-based prediction of local recurrence was attempted by logistic regression and fast and frugal trees. Models were compared using the Akaike information criterion. Results: There were 1500 treatments in 1434 patients; 117 tumors recurred locally. Actuarial local control rates at 12 and 36 months were 96.8% (95% confidence interval, 95.8%-97.8%) and 89.0% (87.0%-91.1%), respectively. In univariable Cox regression, BEDave was the best predictor of risk of local recurrence, and a model based on BEDmin had substantially less evidential support. In univariable logistic regression, the model based on BEDave also performed best. Multivariable classification using fast and frugal trees revealed BEDmax to be the most important predictor, followed by BEDave. Conclusions: BEDave was generally better correlated with tumor control probability than either BEDmax or BEDmin. Because the average between near-minimum and near-maximum doses was highly correlated to the mean gross tumor volume dose, the latter may be used as a prescription target. More emphasis could be placed on achieving sufficiently high mean doses within the gross tumor volume rather than the PTV covering dose, a concept needing further validation. (C) 2020 Elsevier Inc. All rights reserved.
DOI:doi:10.1016/j.ijrobp.2020.03.005
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1016/j.ijrobp.2020.03.005
 DOI: https://doi.org/10.1016/j.ijrobp.2020.03.005
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:clinical-practice
 control probability
 linear-models
 patterns-of-care
 radiotherapy sbrt
 regression
 survival
K10plus-PPN:1747858609
Verknüpfungen:→ Zeitschrift

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