title: Feasibility assessment of the interactive use of a Monte Carlo algorithm in treatment planning for intraoperative electron radiation therapy.
abstract: This work analysed the feasibility of using a fast, customized Monte Carlo (MC) method to perform accurate computation of dose distributions during pre- and intraplanning of intraoperative electron radiation therapy (IOERT) procedures. The MC method that was implemented, which has been integrated into a specific innovative simulation and planning tool, is able to simulate the fate of thousands of particles per second, and it was the aim of this work to determine the level of interactivity that could be achieved. The planning workflow enabled calibration of the imaging and treatment equipment, as well as manipulation of the surgical frame and insertion of the protection shields around the organs at risk and other beam modifiers. In this way, the multidisciplinary team involved in IOERT has all the tools necessary to perform complex MC dosage simulations adapted to their equipment in an efficient and transparent way. To assess the accuracy and reliability of this MC technique, dose distributions for a monoenergetic source were compared with those obtained using a general-purpose software package used widely in medical physics applications. Once accuracy of the underlying simulator was confirmed, a clinical accelerator was modelled and experimental measurements in water were conducted. A comparison was made with the output from the simulator to identify the conditions under which accurate dose estimations could be obtained in less than 3 min, which is the threshold imposed to allow for interactive use of the tool in treatment planning. Finally, a clinically relevant scenario, namely early-stage breast cancer treatment, was simulated with pre- and intraoperative volumes to verify that it was feasible to use the MC tool intraoperatively and to adjust dose delivery based on the simulation output, without compromising accuracy. The workflow provided a satisfactory model of the treatment head and the imaging system, enabling proper configuration of the treatment planning system and providing good accuracy in the dosage simulation.
authors.affiliation: Department of Electronic Engineering, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa, 28040 Madrid, Spain. Biomedical Research Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain.
authors.forename: Pedro
authors.initials: P
authors.lastname: Guerra
authors.forename: José M
authors.initials: JM
authors.lastname: Udías
authors.forename: Elena
authors.initials: E
authors.lastname: Herranz
authors.forename: Juan Antonio
authors.initials: JA
authors.lastname: Santos-Miranda
authors.forename: Joaquín L
authors.initials: JL
authors.lastname: Herraiz
authors.forename: Manlio F
authors.initials: MF
authors.lastname: Valdivieso
authors.forename: Raúl
authors.initials: R
authors.lastname: Rodríguez
authors.forename: Juan A
authors.initials: JA
authors.lastname: Calama
authors.forename: Javier
authors.initials: J
authors.lastname: Pascau
authors.forename: Felipe A
authors.initials: FA
authors.lastname: Calvo
authors.forename: Carlos
authors.initials: C
authors.lastname: Illana
authors.forename: María J
authors.initials: MJ
authors.lastname: Ledesma-Carbayo
authors.forename: Andrés
authors.initials: A
authors.lastname: Santos
mesh_terms: D000465:Algorithms; D004583:Electrons / Q000627:therapeutic use; D005240:Feasibility Studies; D007432:Intraoperative Period; D009010:Monte Carlo Method; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results