A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high-dose-rate (HDR) brachytherapy

• Verfasst von: Guthier, Christian Vinzenz [VerfasserIn]
• Verfasst von: Hesser, Jürgen [VerfasserIn]
• Titel: A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high-dose-rate (HDR) brachytherapy
• Verf.angabe: Christian V. Guthier, Antonio L. Damato, and Akila N. Viswanathan, Juergen W. Hesser, Robert A. Cormack
• E-Jahr: 2017
• Jahr: 20 July 2017
• Umfang: 11 S.
• Fussnoten: Gesehen am 23.7.2018 ; First published: 19 June 2017
• Titel Quelle: Enthalten in: Medical physics
• Ort Quelle: Hoboken, NJ : Wiley, 1974
• Jahr Quelle: 2017
• Band/Heft Quelle: 44(2017), 9, Seite 4452-4462
• ISSN Quelle: 2473-4209
• ISSN Quelle: 1522-8541
• DOI: doi:10.1002/mp.12410
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: brachytherapy
• Sach-SW: dosimetric measures
• Sach-SW: gynecologic HDR
• Sach-SW: inverse treatment planning
• K10plus-PPN: 1577835085

Abstract

Purpose: In this study, we introduce a novel, fast, inverse treatment planning strategy for interstitial high-dose-rate (HDR) brachytherapy with multiple regions of interest solely based on dose-volume-histogram-related dosimetric measures (DMs). Methods: We present a new problem formulation of the objective function that approximates the indicator variables of the standard DM optimization problem with a smooth logistic function. This problem is optimized by standard gradient-based methods. The proposed approach is then compared against state-of-the-art optimization strategies. Results: All generated plans fulfilled prescribed DMs for all organs at risk. Compared to clinical practice, a statistically significant improvement in coverage of target structures was achieved. Simultaneously, DMs representing high-dose regions were significantly reduced. The novel optimization strategies run-time was (0.8 ± 0.3) s and thus outperformed the best competing strategies of the state of the art. In addition, the novel DM-based approach was associated with a statistically significant increase in the number of active dwell positions and a decrease in the maximum dwell time. Conclusions: The generated plans showed a clinically significant increase in target coverage with fewer hot spots, with an optimization time approximately three orders of magnitude shorter than manual optimization currently used in clinical practice. As optimization is solely based on DMs, intuitive, interactive, real-time treatment planning, which motivated the adoption of manual optimization in our clinic, is possible.