Radio therapy

The study shows that transform the radiation-therapy workflow while freeing specialist clinical and technical staff to dedicate more time to patient care. That message will be front and centre for visitors to the booth of RaySearch Laboratories; the Stockholm-based oncology-software company, at the annual congress of the European Society for Radiotherapy and Oncology (ESTRO 38); which gets under way in Milan, Italy, on 26 April.

Clinical and technical staff

The ESTRO conference represents the official European unveiling of RayStation 8B; the latest release of RaySearch’s treatment-planning software. Among a raft of new features; perhaps the most anticipated are the machine-learning innovations that underpin Ray Station’s capabilities in automated organ segmentation in other words; quantitative 3D visualization and automated treatment planning.

The goal is twofold: to deliver workflow efficiencies versus manual treatment planning; with plans delivered in minutes rather than hours; and to generate personalized treatment plans tailored to the unique needs of each patient. (See also “Machine learning: a game-changer for radiation oncology”.)

“Our machine-learning and deep-learning applications will help improve efficiency and consistency in the radiation-oncology clinic; reducing the dependence on an individual planner’s knowledge;” explains Emil Ekström, chief functionality owner for RayStation. What’s more; adds Ekström; “the machine-learning framework in RayStation 8B will facilitate knowledge-sharing; with radiation oncologists and medical physicists able to learn from each other through the machine-learning models.”

The radiation-oncology

Machine learning: a game-changer for radiation therapy Significantly; deployment of the machine-learning models is independent from the version of the treatment-planning software; with RaySearch adding models on a rolling basis so that customers will be able to access them without waiting for a new software release.

Clinics will also be able to train their own models for both segmentation and planning as well as share models with other facilities. “The nature of machine learning makes it possible to share models without the inclusion of personal data and thus creates unique opportunities for collaboration between cancer centres,” says Ekström.

Ekström, for his part; is at the sharp-end of RayStation product development, working alongside a 70-strong cross-functional team comprising computer scientists, UI/UX specialists; physicists, mathematicians and testers all of them co-located at RaySearch’s Stockholm headquarters. Collectively their remit is to translate RayStation product strategy which is owned and articulated at executive board level into continuous improvement and product innovation for clinical customers.

RayStation product development

Users can expect improvements in the dose accuracy compared to the analytical dose engines in cases with large in homogeneities, small fields and for dose out of field,” says Ekström. While the Monte Carlo dose engine for photons will ultimately be made available to all RayStation users, Ekström advises that it will be limited to selected clinical sites for the latest release.
Another prominent theme in RayStation 8B is robustness specifically regarding the software’s “toolbox” for evaluating and comparing treatment plans and plan approval. The creation of robust treatment plans and the evaluation of robustness prior to delivery is one of the main challenges in radiation therapy.