Rainer J Klement1, Michael Allgäuer2, Nicolaus Andratschke3, Oliver Blanck4, Judit Boda-Heggemann5, Karin Dieckmann6, Marciana Duma7, Iris Ernst8, Michael Flentje9, Ute Ganswindt10, Peter Hass11, Christoph Henkenberens12, Detlef Imhoff13, Henning K Kahl14, Robert Krempien15, Fabian Lohaus16, Ursula Nestle17, Meinhard Nevinny-Stickel18, Cordula Petersen19, Vanessa Schmitt20, Sabine Semrau21, Florian Sterzing22, Jan Streblow22, Thomas G Wendt23, Andrea Wittig24, Matthias Guckenberger25. 1. Department of Radiation Oncology, Leopoldina Hospital Schweinfurt, Schweinfurt, Germany. Electronic address: rainer_klement@gmx.de. 2. Department of Radiation Oncology, Barmherzige Brüder, Regensburg, Germany. 3. Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Radiation Oncology, University Medicine Rostock, Germany. 4. Department of Radiation Oncology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany. 5. Department of Radiation Oncology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany. 6. Department of Radiation Oncology, Allgemeines Krankenhaus Wien, Vienna, Austria. 7. Department of Radiation Oncology, Technical University Munich, Munich, Germany. 8. Department of Radiation Oncology, Universitätsklinikum Münster, Münster, Germany. 9. Department of Radiation Oncology, University of Wuerzburg, Wuerzburg, Germany. 10. Department of Radiation Oncology, LMU München, Munich, Germany. 11. Department of Radiation Oncology, Universitätsklinikum Magdeburg, Magdeburg, Germany. 12. Department of Radiotherapy and Special Oncology, Medical School Hannover, Hannover, Germany. 13. Department of Radiation Oncology, Universitätsklinikum Frankfurt am Main, Frankfurt, Germany. 14. Department of Radiation Oncology, Klinikum Augsburg, Augsburg, Germany. 15. Department of Radiation Oncology, Helios Klinikum Berlin Buch, Berlin, Germany. 16. Department of Radiation Oncology, Medical Faculty and University Hospital C.G. Carus, Technische Universität Dresden, Germany. 17. Department of Radiation Oncology, Universitätsklinikum Freiburg, Freiburg, Germany. 18. Department of Therapeutic Radiology and Oncology, Innsbruck Medical University, Innsbruck, Austria. 19. Department of Radiation Oncology, Universitätsklinikum Eppendorf, Hamburg, Germany. 20. Department of Radiation Oncology, Universitätsklinikum Aachen, Germany. 21. Department of Radiation Oncology, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany. 22. Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany. 23. Department of Radiation Oncology, University Hospital Jena, Jena, Germany. 24. Department of Radiotherapy and Radiation Oncology, Philipps-University Marburg, Marburg, Germany. 25. Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Radiation Oncology, University of Wuerzburg, Wuerzburg, Germany.
Abstract
PURPOSE: Most radiobiological models for prediction of tumor control probability (TCP) do not account for the fact that many events could remain unobserved because of censoring. We therefore evaluated a set of TCP models that take into account this censoring. METHODS AND MATERIALS: We applied 2 fundamental Bayesian cure rate models to a sample of 770 pulmonary metastasis treated with stereotactic body radiation therapy at German, Austrian, and Swiss institutions: (1) the model developed by Chen, Ibrahim and Sinha (the CIS99 model); and (2) a mixture model similar to the classic model of Berkson and Gage (the BG model). In the CIS99 model the number of clonogens surviving the radiation treatment follows a Poisson distribution, whereas in the BG model only 1 dominant recurrence-competent tissue mass may remain. The dose delivered to the isocenter, tumor size and location, sex, age, and pretreatment chemotherapy were used as covariates for regression. RESULTS: Mean follow-up time was 15.5 months (range: 0.1-125). Tumor recurrence occurred in 11.6% of the metastases. Delivered dose, female sex, peripheral tumor location and having received no chemotherapy before RT were associated with higher TCP in all models. Parameter estimates of the CIS99 were consistent with the classical Cox proportional hazards model. The dose required to achieve 90% tumor control after 15.5 months was 146 (range: 114-188) Gy10 in the CIS99 and 133 (range: 101-164) Gy10 in the BG model; however, the BG model predicted lower tumor control at long (≳20 months) follow-up times and gave a suboptimal fit to the data compared to the CIS99 model. CONCLUSIONS: Biologically motivated cure rate models allow adding the time component into TCP modeling without being restricted to the follow-up period which is the case for the Cox model. In practice, application of such models to the clinical setting could allow for adaption of treatment doses depending on whether local control should be achieved in the short or longer term.
PURPOSE: Most radiobiological models for prediction of tumor control probability (TCP) do not account for the fact that many events could remain unobserved because of censoring. We therefore evaluated a set of TCP models that take into account this censoring. METHODS AND MATERIALS: We applied 2 fundamental Bayesian cure rate models to a sample of 770 pulmonary metastasis treated with stereotactic body radiation therapy at German, Austrian, and Swiss institutions: (1) the model developed by Chen, Ibrahim and Sinha (the CIS99 model); and (2) a mixture model similar to the classic model of Berkson and Gage (the BG model). In the CIS99 model the number of clonogens surviving the radiation treatment follows a Poisson distribution, whereas in the BG model only 1 dominant recurrence-competent tissue mass may remain. The dose delivered to the isocenter, tumor size and location, sex, age, and pretreatment chemotherapy were used as covariates for regression. RESULTS: Mean follow-up time was 15.5 months (range: 0.1-125). Tumor recurrence occurred in 11.6% of the metastases. Delivered dose, female sex, peripheral tumor location and having received no chemotherapy before RT were associated with higher TCP in all models. Parameter estimates of the CIS99 were consistent with the classical Cox proportional hazards model. The dose required to achieve 90% tumor control after 15.5 months was 146 (range: 114-188) Gy10 in the CIS99 and 133 (range: 101-164) Gy10 in the BG model; however, the BG model predicted lower tumor control at long (≳20 months) follow-up times and gave a suboptimal fit to the data compared to the CIS99 model. CONCLUSIONS: Biologically motivated cure rate models allow adding the time component into TCP modeling without being restricted to the follow-up period which is the case for the Cox model. In practice, application of such models to the clinical setting could allow for adaption of treatment doses depending on whether local control should be achieved in the short or longer term.
Authors: Susanne Stera; Panagiotis Balermpas; Mark K H Chan; Stefan Huttenlocher; Stefan Wurster; Christian Keller; Detlef Imhoff; Dirk Rades; Jürgen Dunst; Claus Rödel; Guido Hildebrandt; Oliver Blanck Journal: Strahlenther Onkol Date: 2017-09-05 Impact factor: 3.621
Authors: Rene Baumann; Mark K H Chan; Florian Pyschny; Susanne Stera; Bettina Malzkuhn; Stefan Wurster; Stefan Huttenlocher; Marcella Szücs; Detlef Imhoff; Christian Keller; Panagiotis Balermpas; Dirk Rades; Claus Rödel; Jürgen Dunst; Guido Hildebrandt; Oliver Blanck Journal: Front Oncol Date: 2018-05-17 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