A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high‐dose‐rate (HDR) brachytherapy. Issue 9 (20th July 2017)

• Record Type: Journal Article
• Authors: Guthier, Christian V.; Damato, Antonio L.; Viswanathan, Akila N.; Hesser, Juergen W.; Cormack, Robert A.
• Is Part Of: Medical physics. Volume 44:Issue 9(2017)
• Page Start: 4452
• Page End: 4462
• Publication Date: 2017-07-20
• Subjects: brachytherapy -- dosimetric measures -- gynecologic HDR -- inverse treatment planning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.12410
• Languages: English
• ISSNs: 0094-2405

Abstract

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 ( p = 0.01 ) in coverage of target structures was achieved. Simultaneously, DMs representing high‐dose regions were significantly reduced ( p = 0.01 ) . 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 ( p = 0.01 ) 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.