RaySearch releases first machine learning applications in RayStation treatment planning system

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The machine learning and deep learning applications in RayStation 8B are another step in RaySearch’s continuous work to advance cancer care. Machine learning means that the algorithms in these features learn from the data they are trained on, and they learn in a way that resembles human logic.

The applications for automated treatment planning and automated organ segmentation will help improve efficiency and consistency in the clinic. The machine learning framework delivered with RayStation 8B allows for models to be trained on the clinic’s available data, and it is also possible to use pre-trained models provided by RaySearch.

The ground-breaking features have been developed by RaySearch’s in-house machine learning department in collaboration with Princess Margaret Cancer Centre in Toronto, Canada, and are the first machine learning applications in a treatment planning system on the radiation oncology market today.

Robust evaluation is a new module in Plan Evaluation, which enables efficient evaluation of robustness of treatment plans with respect to uncertainties in patient setup and density interpretation of CT. Multiple scenarios with different uncertainty settings are easily created and the scenarios can be evaluated simultaneously for quick decision support. This is achieved through display of different robustness metrics connected to the clinical goals of the treatment.

Improvements for photon planning include a Monte Carlo dose engine. The Monte Carlo dose algorithm brings improved accuracy and it is utilizing the GPU to enable fast dose computation. The dose for a dual arc VMAT plan can be computed in less than 60 seconds, which is at least one order of magnitude faster than any other system on the market. The Monte Carlo dose engine can be used also during optimization.

Boron neutron capture therapy (BNCT) planning has been under development since 2017 and is now available in RayStation 8B. BNCT is a unique type of radiation therapy that enables targeting of cancer at the cellular level. BNCT planning in RayStation is developed together with Sumitomo Heavy Industries, Ltd and Neutron Therapeutics, Inc.

RayStation 8B also adds various improvements and enhancements, including support for directly deliverable MCO for VMAT, collimation of individual energy layers with the Adaptive Aperture™ of the Mevion S250i HYPERSCAN proton therapy system, and handling of relative biological effectiveness for proton dose.

Johan Löf, CEO of RaySearch, says: "The release of RayStation 8B is the result of our hard work towards more extensive automatic features within oncology software. This brings machine learning and deep learning features to the market for the first time in a TPS. The features include state of the art neural network architecture and there are significant differences compared to existing automation. They are faster, may generalize better and it is easy to share machine learning models. The two features work well together and are ideal for adaptive workflows as they are both fast and consistent. It is going to be very interesting to see how far ahead our users can advance with these new powerful tools. The evaluation of robustness, Monte Carlo dose engine for photons and BNCT planning are all very exciting new features as well.”  

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RaySearch releases first machine learning applications in RayStation treatment planning system.  Appl Rad Oncol. 

By News Release| January 08, 2019
Categories:  Section|Technologies

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