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RadCalc’s Monte Carlo capability streamlines and automates 3D dose-volume verification

21 Jun 2023 Sponsored by LAP

Monte Carlo 3D dose-check calculations help clinical physicists maintain confidence in their patient-specific QA for hard-to-treat disease indications

The AUSL-IRCCS medical physics team
Independent viewpoint: given the clinical emphasis on hypofractionation, the AUSL-IRCCS medical physics team relies heavily on RadCalc’s Monte Carlo software module as part of the patient QA workflow. (Courtesy: AUSL-IRCCS)

Secondary dose calculations represent a foundational building block for any patient-specific quality-assurance (QA) programme, providing at-scale validation of radiotherapy treatment plans in the radiation oncology clinic. Front-and-centre in the patient QA endeavour – and in daily use at over 2300 cancer treatment centres worldwide – is LAP’s RadCalc QA secondary check software which, for more than two decades, has provided medical physicists and dosimetrists with automated and independent dosimetric verification of their radiotherapy treatment planning systems (TPS).

An early-adopter and RadCalc devotee is Mauro Iori, director of medical physics at the Institute in Advanced Technologies and Models of Care in Oncology (IRCCS), part of the Azienda Unità Sanitaria Locale (AUSL) di Reggio Emilia in northern Italy. “We have been using RadCalc, in its various iterations, to support independent patient QA for more than 20 years – helping us to reduce our direct dosimetry QA measurements along the way,” explains Iori. “Thanks to LAP’s extensive user base, the software provides a stable, robust and uniform QA environment that integrates seamlessly with our TPS. RadCalc is also vendor-agnostic, so users can standardize the second-check QA workflow across different treatment systems and modalities.”

Monte Carlo insights

Operationally, Iori is one of five clinical physicists and three technicians (dosimetrists) within the AUSL-IRCCS radiation oncology programme, overseeing a suite of two Varian TrueBeam machines, an Accuray TomoTherapy system, a high-dose-rate Elekta brachytherapy unit and an orthovoltage X-ray device. “We treat over 1600 patients each year and cover a wide range of disease indications,” notes Iori (who also manages six other medical physicists and four technicians working in the AUSL-IRCCS radiology and nuclear medicine departments). “What’s more,” he adds, “around 65% of our external-beam radiotherapy treatments involve some form of hypofractionation.” Put simply, that means increased dose per fraction to enable significantly improved patient experience and increased patient throughput – all part of an operational drive for enhanced workflow efficiency.

Mauro Iori

Given AUSL-IRCCS’s clinical emphasis on hypofractionation, Iori and colleagues rely heavily on RadCalc’s Monte Carlo software module for automated 3D dose-volume verification. The goal is to maintain confidence in QA process accuracy across harder-to-treat clinical indications and thereby ensure the planning treatment volume is being covered, while guaranteeing plan quality by comparing dose to adjacent critical structures and organs-at-risk (OARs). Equally important, the medical physics team needs to know if something is not right straight away when treating patients with escalated dose per fraction – in the case of a machine error, for example, or incorrect patient set-up. “For this type of check,” notes Iori, “the log-file analysis and in vivo dosimetry modules in RadCalc constitute a complementary and dedicated tool.”

Under the hood, RadCalc’s Monte Carlo module relies on BEAMnrc (a well-established simulation system for external-beam sources in radiotherapy) utilizing proprietary machine modelling acquired by LAP from McGill University in Canada. The software’s 3D functionality is reinforced by RadCalcAIR (Automated Import and Report) to give users a fully automated second-check process with percent difference, dose-volume histogram (DVH), protocol metrics, gamma and many more customizable tools.

Shedding light on complexity

By allowing 3D verification of plan dose distribution, says Iori, the Monte Carlo module comes into its own for more challenging treatment planning scenarios. Examples include advanced head-and-neck cancers and late-stage prostate and rectal disease – indications that often require larger, heavily modulated treatment fields that are problematic in terms of conventional point-dose QA checks.

Another Monte Carlo clinical use-case arises when treating small or complex tumours surrounded by heterogeneities (e.g. in the lung, abdominal cavities as well as adjacent to bone or metal implants). Planning techniques with steep dose gradients, for example, are especially relevant for lung stereotactic treatments, with the tumour targets generally located near the chest wall, heart and normal blood vessels. Here RadCalc’s Monte Carlo module can perform an accurate and realistic dose verification of TPS plans – implementation of the machine models in BEAMnrc, with every physical component included, establishing confidence in such challenging cases.

The QA workflow in this scenario is all about streamlining: the physicist simply exports the treatment plan via their DICOM RT and RadCalc will automatically verify the plan using a Monte Carlo algorithm, generating results in minutes. If the treatment plan doesn’t pass various preset criteria, RadCalc will prompt the user to investigate what’s going on using a suite of dose analysis tools before determining the course of action (in terms of further preclinical QA or adding in vivo dosimetry checks on the treatment machine).

“With RadCalc’s Monte Carlo tools we can achieve the highest quality of dosimetric verification,” claims Iori, “and not only for simple treatment plans but complex planning scenarios as well, or in the case of adaptive radiotherapy treatments. Right now, we use the Monte Carlo calculations for around 30% of our external-beam radiotherapy patients – chiefly for head-and-neck, thorax and pelvis disease indications that exhibit the highest levels of tissue heterogeneity. Over time, we will extend the use of the Monte Carlo module to all treatment plans.”

The clinical end-game? Better targeting accuracy and dose distribution accuracy – and, ultimately, enhanced treatment outcomes for AUSL-IRCCS cancer patients.

Calculation, simulation, validation

The roll-out of the RadCalc 3D Monte Carlo module at AUSL-IRCCS was preceded by a period of preclinical “tuning and validation”, with the optimized software subsequently used to dosimetrically verify complex treatment plans where the measured dose distributions can be inaccurate due to the TPS dose calculation algorithm.

During the commissioning phase, the AUSL-IRCCS medical physics team, working with colleagues from the University of Bologna in Italy, built Monte Carlo models on the back of specific commissioning measurements. To set up the Monte Carlo module, the team loaded a file containing dosimetric data for different beam energies (6X, 6FFF, 10X, 10FFF) into RadCalc and prepopulated it with values obtained directly from phantom measurements (using defined protocols for percentage depth dose and off-axis ratio).

Another key step involved optimization of the Additional Radiation to Light Field Offset (ARLF) tuning parameter, with Monte Carlo simulations performed on a uniform phantom for four different ARLF values (for each considered energy). The goal here was to achieve the best dose-comparison agreements between Monte Carlo simulations and the volumetric patient-specific QA measurements (with phantom dose distributions and calculated results evaluated in terms of 2 mm/2% gamma pass rate).

“Our preclinical study showed good agreement between Radcalc Monte Carlo simulations and dose measurements, enhancing the dosimetric performance of the secondary-check tool used to verify our treatment plans,” explained Iori. “Following validation, RadCalc’s Monte Carlo module now enables us to better estimate the plan doses in lung-cancer patients and to detect possible inaccuracies due to tissue homogeneity, which are not quantifiable using homogeneous phantoms.”

Further reading

Giadi Sceni et al. 2023 Tuning and validation of the new RadCalc 3D Monte Carlo-based pre-treatment plan verification tool Journal of Mechanics in Medicine and Biology

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