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  4. Machine learning

INTELLIGENT
TREATMENT PLANNING

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

  • Deep learning segmentation of CT structures
  • Deep learning based dose prediction for automated treatment plan generation
  • Deep learning solutions are integrated into RayStation
  • Validated models are released with RayStation releases
  • Spend less time on repetitive tasks
  • Have more time for patient consultations and complex cases

WEBINAR: AI in Cancer Care: AI in Everyday Radiotherapy Practice

 
 

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Deep Learning Segmentation is included in RayStation*

RaySearch is continuously improving the released models and will release new or updated Deep Learning* Segmentation (DLS) models regularly, with RayStation releases.

Deep learning capabilities in RayStation®* help make image segmentation quicker and more consistent. A high-speed GPU-powered algorithm is capable of producing consistent segmentation results using guideline-based segmentation models that have been trained and evaluated on curated data for different body sites. 

 

*Applies for RayStation 11B and later versions

*Subject to regulatory clearance in some markets.

WHITE PAPER: DEEP LEARNING SEGMENTATION

Click here to download and view

Webinar: Artificial Intelligence in Cancer Care: AI for Medical Image Segmentation

We invite you to watch this RaySearch Webinars series: Artificial Intelligence in Cancer Care. Over two insightful sessions, experts from our Machine
Learning team will offer an inside look at the meticulous, supervised
development process behind our AI-driven solutions for medical image
segmentation and dose prediction.

 
 

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HIGHLIGHTS

The world’s first machine learning 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. See the study here

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 in minutes.

WHITE PAPER: DEEP LEARNING PLANNING

Click here to view

RaySearch Webinars: AI in Radiotherapy Planning

In the second session of the AI in cancer care webinar series, the focus was on AI's role in radiotherapy planning, featuring presenters Frederic and Christian. The session engaged participants with live polls and discussions about the integration of AI in healthcare, particularly in automating oncology tasks. Attendees learned about RaySearch's advancements in AI since 2017, including deep learning planning and its application in clinical workflows. The importance of quality assurance and validation for AI solutions was emphasized, alongside the need for continuous support for clinical users. The webinar concluded with an invitation for further engagement and upcoming sessions.

 
 

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Highlights

What's new in RayStation v2026?

The new release continues to expand RayStation’s machine learning capabilities, with both segmentation and planning improvements.

  • More than 30 new structures have been added to the deep learning segmentation portfolio, including bowel structures, female pelvic anatomies, prostate bed, bronchial tree, pediatric support, and more.
  • Three new deep learning planning models have been introduced, covering breast locoregional, lung proton, and prostate treatments.

These additions enable faster contouring and more automated, high-quality treatment planning across a broader range of indications.

Testimonials from customers

“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

WEBINAR: AI Model for Head and Neck Autoplanning - Kujtim Latifi

This video presents a research-driven approach to automating head and neck radiotherapy planning using deep learning, featuring insights from Kritim Latifi at Moffitt Cancer Center. The discussion covers the clinic's stringent dose requirements, challenges with traditional auto-planning tools, and the development of a deep learning model that produces clinically acceptable plans in under 30 minutes. Results show promising performance, especially for smaller targets, with ongoing work to improve robustness and fully automate adaptive workflows for head and neck cases.
RAYSEARCH WEBINARS: Learning together, leading together. 

 
 

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Spotlight Series, Episode 2: Shaping the future of cancer treament planning by leveraging AI

 

Explore the transformative power of AI-driven automation in cancer treatment planning and care. Join Fredrik Löfman, Head of Machine Learning at RaySearch Laboratories, as he discusses how AI enhances patient and practitioner experiences by saving time, boosting efficiency and increasing treatment accuracy.

In Episode 2, Fredrik delves into AI’s vital role in helping healthcare professionals define optimal cancer treatment. With RaySearch innovations, clinicians can leverage AI to analyze patient and treatment data, delivering knowledge-based treatment alternatives through deep learning*.

Looking ahead, consider making more informed decisions that weigh radiotherapy against other treatment modalities, or treatment plans based on real-world evidence that aggregate patient data, treatment data, outcome data from clinics worldwide.

*Subject to regulatory clearance in some markets

See more from the RaySearch Spotlight series here.

 
 

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