Fernando Pizarro1, Araceli Hernández2,3. 1. 1 Department of Medical Physics, University Hospital of Burgos, Burgos, Spain. 2. 2 Department of Medical Physics, Clinical Hospital of Zaragoza, Zaragoza, Spain. 3. 3 Department of Radiology, Pediatrics and Physical Medicine, University of Zaragoza, Zaragoza, Spain.
Abstract
OBJECTIVE: To present a method for optimizing radiotherapy fractionation schedules using radiobiological tools and taking into account the patient´s dose-volume histograms (DVH). METHODS: This method uses a figure of merit based on the uncomplicated tumour control probability (P+) and the generalized equivalent uniform dose (gEUD). A set of doses per fraction is selected in order to find the dose per fraction and the total dose, thus maximizing the figure of merit and leading to a biologically effective dose that is similar to the prescribed schedule. RESULTS: As a clinical example, a fractionation schedule for a prostate treatment plan is optimized and presented herein. From a prescription schedule of 70 Gy/35 × 2 Gy, the resulting optimal schema, using a figure of merit which only takes into account P+, is 54.4 Gy/16 × 3.4 Gy. If the gEUD is included in that figure of merit, the result is 65 Gy/26 × 2.5 Gy. Alternative schedules, which include tumour control probability (TCP) and the normal tissue complication probability (NTCP) values are likewise shown. This allows us to compare different schedules instead of solely finding the optimal value, as other possible clinical factors must be taken into account to make the best decision for treatment. CONCLUSION: The treatment schedule can be optimized for each patient through radiobiological analysis. The optimization process shown below offers physicians alternative schedules that meet the objectives of the prescribed radiotherapy. Advances in knowledge: This article provides a simple, radiobiological-function-based method to take advantage of a patient's dose-volume histograms in order to better select the most suitable treatment schedule.
OBJECTIVE: To present a method for optimizing radiotherapy fractionation schedules using radiobiological tools and taking into account the patient´s dose-volume histograms (DVH). METHODS: This method uses a figure of merit based on the uncomplicated tumour control probability (P+) and the generalized equivalent uniform dose (gEUD). A set of doses per fraction is selected in order to find the dose per fraction and the total dose, thus maximizing the figure of merit and leading to a biologically effective dose that is similar to the prescribed schedule. RESULTS: As a clinical example, a fractionation schedule for a prostate treatment plan is optimized and presented herein. From a prescription schedule of 70 Gy/35 × 2 Gy, the resulting optimal schema, using a figure of merit which only takes into account P+, is 54.4 Gy/16 × 3.4 Gy. If the gEUD is included in that figure of merit, the result is 65 Gy/26 × 2.5 Gy. Alternative schedules, which include tumour control probability (TCP) and the normal tissue complication probability (NTCP) values are likewise shown. This allows us to compare different schedules instead of solely finding the optimal value, as other possible clinical factors must be taken into account to make the best decision for treatment. CONCLUSION: The treatment schedule can be optimized for each patient through radiobiological analysis. The optimization process shown below offers physicians alternative schedules that meet the objectives of the prescribed radiotherapy. Advances in knowledge: This article provides a simple, radiobiological-function-based method to take advantage of a patient's dose-volume histograms in order to better select the most suitable treatment schedule.
Authors: Stefan Höcht; Daniel M Aebersold; Clemens Albrecht; Dirk Böhmer; Michael Flentje; Ute Ganswindt; Tobias Hölscher; Thomas Martin; Felix Sedlmayer; Frederik Wenz; Daniel Zips; Thomas Wiegel Journal: Strahlenther Onkol Date: 2016-09-14 Impact factor: 3.621
Authors: Simon K B Spohn; Ilias Sachpazidis; Rolf Wiehle; Benedikt Thomann; August Sigle; Peter Bronsert; Juri Ruf; Matthias Benndorf; Nils H Nicolay; Tanja Sprave; Anca L Grosu; Dimos Baltas; Constantinos Zamboglou Journal: Front Oncol Date: 2021-05-14 Impact factor: 6.244