Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol.

Autor: Monticelli D; Università degli Studi di Milano, Milano, Italy; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy., Castriconi R; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy. Electronic address: castriconi.roberta@hsr.it., Tudda A; Università degli Studi di Milano, Milano, Italy; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy., Fodor A; Department of Radiation Oncology, IRCCS San Raffaele Scientific Institute, Milano, Italy., Deantoni C; Department of Radiation Oncology, IRCCS San Raffaele Scientific Institute, Milano, Italy., Gisella Di Muzio N; Department of Radiation Oncology, IRCCS San Raffaele Scientific Institute, Milano, Italy., Mangili P; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy., Del Vecchio A; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy., Fiorino C; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy., Broggi S; Medical Physics Department, IRCCS San Raffaele Scientific Institute, Milano, Italy.
Jazyk: angličtina
Zdroj: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2023 Jun; Vol. 110, pp. 102606. Date of Electronic Publication: 2023 May 15.
DOI: 10.1016/j.ejmp.2023.102606
Abstrakt: Purpose: To extend the knowledge-based (KB) automatic planning approach to CyberKnife in the case of Stereotactic Body Radiation Therapy (SBRT) for prostate cancer.
Methods: Seventy-two clinical plans of patients treated according to the RTOG0938 protocol (36.25 Gy/5fr) with CyberKnife were exported from the CyberKnife system to Eclipse to train a KB-model using the Rapid Plan tool. The KB approach provided dose-volume objectives for specific OARs only and not PTV. Bladder, rectum and femoral heads were considered in the model. The KB-model was successfully trained on 51 plans and then validated on 20 new patients. A KB-based template was tuned in the Precision system for both sequential optimization (SO) and VOLO optimization algorithms. Plans of the validation group were re-optimized (KB-TP) using both algorithms without any operator intervention and compared against the original plans (TP) in terms of OARs/PTV dose-volume parameters. Paired Wilcoxon signed-rank tests were performed to assess statistically significant differences (p < 0.05).
Results: Regarding SO, automatic KB-TP plans were generally better than or equivalent to TP plans. PTVs V95% was slightly worse while OARs sparing for KB-TP was significantly improved. Regarding VOLO optimization, the PTVs coverage was significantly better for KB-TP while there was a limited worsening in the rectum. A significant improvement was observed in the bladder in the range of low-intermediate doses.
Conclusions: An extension of the KB optimization approach to the CyberKnife system has been successfully developed and validated in the case of SBRT prostate cancer.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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