Rainer J Klement1, Jan-Jakob Sonke2, Michael Allgäuer3, Nicolaus Andratschke4, Steffen Appold5, José Belderbos2, Claus Belka6, Oliver Blanck7, Karin Dieckmann8, Hans T Eich9, Frederick Mantel10, Michael Eble11, Andrew Hope12, Anca L Grosu13, Meinhard Nevinny-Stickel14, Sabine Semrau15, Reinhart A Sweeney16, Juliane Hörner-Rieber17, Maria Werner-Wasik18, Rita Engenhart-Cabillic19, Hong Ye20, Inga Grills20, Matthias Guckenberger4. 1. Department of Radiotherapy and Radiation Oncology, Leopoldina Hospital Schweinfurt, Schweinfurt, Germany. Electronic address: rainer_klement@gmx.de. 2. Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands. 3. Department of Radiotherapy, Barmherzige Brüder Regensburg, Regensburg, Germany. 4. Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland. 5. Department of Radiation Oncology, Technische Universität Dresden, Dresden, Germany. 6. Department of Radiation Oncology, University Hospital of Ludwig-Maximilians-University Munich, Munich, Germany. 7. Department of Radiation Oncology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany. 8. Department of Radiotherapy, Medical University of Vienna, Vienna, Austria. 9. Department of Radiotherapy, University Hospital Münster, Münster, Germany. 10. Department of Radiotherapy and Radiation Oncology, University Hospital Wuerzburg, Wuerzberg, Germany. 11. Department of Radiation Oncology, RWTH Aachen University, Aachen, Germany. 12. Department of Radiation Oncology, University of Toronto and Princess Margaret Cancer Center, Toronto, Canada. 13. Department of Radiation Oncology, University Hospital Freiburg, Freiburg, Germany. 14. Department of Radiation Oncology, Medical University Innsbruck, Innsbruck, Austria. 15. Department of Radiation Oncology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany. 16. Department of Radiotherapy and Radiation Oncology, Leopoldina Hospital Schweinfurt, Schweinfurt, Germany. 17. Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany. 18. Department of Radiation Oncology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania. 19. Department of Radiotherapy and Radiation Oncology, Phillips-University Marburg, Marburg, Germany. 20. Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, Michigan.
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.
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.
Authors: Sebastian Regnery; Carolin Buchele; Fabian Weykamp; Moritz Pohl; Philipp Hoegen; Tanja Eichkorn; Thomas Held; Jonas Ristau; Carolin Rippke; Laila König; Michael Thomas; Hauke Winter; Sebastian Adeberg; Jürgen Debus; Sebastian Klüter; Juliane Hörner-Rieber Journal: Front Oncol Date: 2022-01-11 Impact factor: 6.244
Authors: L Wilke; C Moustakis; O Blanck; D Albers; C Albrecht; Y Avcu; R Boucenna; K Buchauer; T Etzelstorfer; C Henkenberens; D Jeller; K Jurianz; C Kornhuber; M Kretschmer; S Lotze; K Meier; P Pemler; A Riegler; A Röser; D Schmidhalter; K H Spruijt; G Surber; V Vallet; R Wiehle; J Willner; P Winkler; A Wittig; M Guckenberger; S Tanadini-Lang Journal: Strahlenther Onkol Date: 2021-07-01 Impact factor: 3.621