News | Treatment Planning | September 23, 2019

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


Related Content

News | Prostate Cancer

July 8,2026 — CorePlus, Puerto Rico’s fully digital precision pathology and clinical laboratory, has announced the ...

Time July 08, 2026
arrow
News | Women's Health

July 1, 2026 — Despite declining birth rates worldwide, the complexity of pregnancy is increasing. Advanced maternal age ...

Time July 01, 2026
arrow
News | Information Technology

June 26, 2026 — Radin Health recently announced the successful deployment of its cloud-native platform at four ...

Time June 26, 2026
arrow
News | FDA

June 25, 2026 — Aidoc recently announced that the U.S. Food and Drug Administration (FDA) granted Breakthrough Device ...

Time June 25, 2026
arrow
News | Mammography

June 23, 2026 — Using artificial intelligence (AI), researchers found that image-based risk scores for breast cancer ...

Time June 24, 2026
arrow
News | Pediatric Imaging

June 16, 2026 — Crescom has officially launched a global clinical Proof of Concept (PoC) of its pediatric ...

Time June 24, 2026
arrow
News | Information Technology

June 24, 2026 — HOPPR Presto Agent (Presto) is now commercially available from HOPPR. Presto iis a tool that ntegrates ...

Time June 24, 2026
arrow
News | Digital Pathology

June 17, 2026 — Proscia has introduced the Fifth Generation of its Concentriq1 platform, helping pathologists focus on ...

Time June 22, 2026
arrow
News | Information Technology

June 9, 2026 — Mosaic Clinical Technologies, a wholly owned subsidiary of Radiology Partners, has launched Mosaic ...

Time June 15, 2026
arrow
News | Imaging Software Development

June 10, 2026 — DeepHealth, Inc., a wholly owned subsidiary of RadNet, has launched Reporting Pro, an AI-powered ...

Time June 12, 2026
arrow
Subscribe Now