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Machine Learning

Machine learning for intelligent treatment planning

Cancer treatment represents one of the most exciting applications of data analytics and machine learning technologies. Machine learning already supports the identification of diseases and diagnosis, through to treatment planning and aftercare. By analyzing thousands of different data points, advanced algorithms in RayStation* can help clinics save time and increase consistency by automating plan generation and organ segmentation.

*Subject to regulatory clearance in some markets.

Key Features

  • Generate personalized treatment plans in minutes
  • Create organ contours in under 45 seconds with deep neural networks
  • Train your own models and
  • Benefit from models trained at other leading cancer clinics
  • Share your models with other clinics
  • Spend less time on repetitive tasks
  • Get more time for patient consultations and complex cases
 

Deep learning capabilities in RayStation help make organ segmentation quicker and more consistent. A high-speed GPU-powered algorithm is capable of producing consistent segmentation results using segmentation models that have been trained and evaluated on clinical data for different body sites. A deep learning model can segment organs in under 45 seconds.

WHITE PAPER: DEEP-LEARNING SEGMENTATION

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Machine learning models for planning and segmentation will be provided by RaySearch continuously, so you don’t need to wait for a new RayStation release to access them. You can also train your own models for treatment planning and organ segmentation. Share and compare models free of personal data with other physicians and clinics anywhere in the world.

The world’s first machine learning treatment plan generation module

RaySearch partnered with Princess Margaret Cancer Center in Canada to develop the world’s first machine learning treatment plan generation module. In May 2019, patients with localized prostate cancer were treated using machine learning treatment plans generated in RayStation as part of a compensative evaluation study.

RaySearch received 510(k) clearance from the U.S. Food and Drug Administration for RayStation 8B , which was the first machine learning applications in a treatment planning system on the radiation oncology market today.

Clinics can get personalized treatment plans in RayStation to benefit from the experience of one of the world’s leading cancer centers, generated in minutes by selecting a pre-trained machine learning model. One or multiple deliverable treatment plans can be automatically generated with varying target/OAR tradeoffs.

RaySearch licenses UHN automation technologies

University Health Network (UHN) in Canada exclusively licensed its advanced technology to RaySearch for incorporation into RayStation. The technology was developed by the Techna Institute, which is a collaboration between UHN and the University of Toronto. Watch this video to get insights into licensed techniques that have led to machine learning algorithms from Princess Margaret Cancer Center being integrated into RayStation.

 

WHITE PAPER: MACHINE LEARNING AUTOMATED PLANNING

Your data is handled with discretion, read our privacy statement

Data-driven radiation oncology

Fredrik Löfman, head of machine learning at RaySearch, discusses how our company is pioneering ways to enable clinicians to access smarter and faster oncology software.

 

INTERVIEW WITH FREDRIK LÖFMAN

Physics World met up with Fredrik Löfman, Head of Machine Learning at RaySearch, at 2019 ASTRO Annual Meeting in Chicago to discuss how machine learning capabilities in RayStation can help revolutionize treatment planning.

 

“Machine learning is a natural fit for automating the complex treatment planning process. We expect it will enable us to generate highly personalized radiation treatment plans more efficiently, thereby allowing clinical resources or specialist technical staff to dedicate more time to patient care. Deemed clinically acceptable by experts around the world, RayStation algorithms generate high-quality treatment plans that are preferred or deemed equivalent to clinical plans.”

Tom Purdie
Medical Physicist, Princess Margaret Cancer Center