Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning.
PURPOSE
This work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.


METHODS
The MLC-equipped M6 Cyberknife was modelled and integrated in Moderato, our in-house platform offering independent verification of radiotherapy dose distributions. The model was validated by comparing TPS dose distributions with Moderato and by film measurements. Using this model, a machine learning algorithm was trained to find electron beam parameters for other M6 devices, by simulating dose curves with varying spot size and energy. The algorithm was optimized using cross-validation and tested with measurements from other institutions equipped with a M6 Cyberknife.


RESULTS
Optimal agreement in the Monte Carlo model was reached for a monoenergetic electron beam of 6.75 MeV with Gaussian spatial distribution of 2.4 mm FWHM. Clinical plan dose distributions from Moderato agreed within 2% with the TPS, and film measurements confirmed the accuracy of the model. Cross-validation of the prediction algorithm produced mean absolute errors of 0.1 MeV and 0.3 mm for beam energy and spot size respectively. Prediction-based simulated dose curves for other centres agreed within 3% with measurements, except for one device where differences up to 6% were detected.


CONCLUSIONS
The M6 Cyberknife was integrated in Moderato and validated through dose re-calculations and film measurements. The prediction algorithm was successfully applied to obtain electron beam parameters for other M6 devices. This method would prove useful to speed up modelling of new machines in Monte Carlo systems.
['Wagner|A|A|', 'Brou Boni|K|K|', 'Rault|E|E|', 'Crop|F|F|', 'Lacornerie|T|T|', 'Van Gestel|D|D|', 'Reynaert|N|N|']
D003198:Computer Simulation D004867:Equipment Design D006801:Humans D000069550:Machine Learning D008954:Models, Biological D009010:Monte Carlo Method D010315:Particle Accelerators D011879:Radiotherapy Dosage D011880:Radiotherapy Planning, Computer-Assisted D050397:Radiotherapy, Intensity-Modulated D014057:Tomography, X-Ray Computed D014965:X-Rays
Cyberknife Monte Carlo Machine learning Treatment planning