News | Treatment Planning | September 23, 2019

First Patients Treated With Machine Learning-generated Plans from RayStation 8B

Patients treated at Princess Margaret Cancer Centre in Toronto, Canada, as part of comprehensive evaluation study

First Patients Treated With Machine Learning-generated Plans from RayStation 8B

September 23, 2019 — The first-ever patient radiation therapy treatments generated with machine learning in the RayStation treatment planning system (TPS) have been conducted. Patients with localized prostate cancer are being treated with the technology at the Princess Margaret Cancer Centre in Toronto, Canada, as part of a comprehensive evaluation study.

RaySearch released the machine learning features in RayStation 8B in late December 2018. This technology has been developed by RaySearch’s in-house machine learning department in collaboration with researchers at the Princess Margaret Cancer Centre and Techna Institute, crystalizing years of cutting-edge research led by medical physicist Tom Purdie, Ph.D., and computer scientist Chris McIntosh, Ph.D. The features represent the first applications of machine learning in a TPS on the radiation oncology market, according to RaySearch, producing high-quality radiation treatment plans in only minutes, without the need for any user intervention.

Since May 2019, every patient with localized prostate cancer treated at the Princess Margaret has been part of a prospective initiative under the direction of radiation oncologist Alejandro Berlin, M.D. The initiative was launched after observing excellent clinical results in a retrospective evaluation study conducted during 2018, in which machine learning plans were preferred or deemed equivalent to previous manual plans based on three blinded expert reviewers in 94 percent of cases.

The ongoing phase of this study presents physicians with two blinded treatment plans: a manually generated plan and a machine learning plan. The selected plan undergoes standard peer-review and quality assurance, and then patients proceed to treatment delivery with the preferred plan.

This worldwide endeavor will provide unique data to quantify the performance and preferability of machine learning plans in the real-world environment.

Berlin said, “It has been really exciting for the team to help materialize this machine learning advancement in the radiation oncology field, including deployment into the clinical realm. Our positive results to date validate our observations about the robustness of this planning solution”.

For more information: www.raysearchlabs.com

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