Save time and reduce the risk of error by automating treatment planning. Advanced plan-generating protocols, scripting, automated breast planning, fallback planning, machine learning and plan explorer are some of the tools available in RayStation — the first treatment planning system to incorporate machine learning applications.
RayStation automates parts of the treatment planning process with support for tools such as templates and plan-generation protocols. These are the steps in the planning process which can be automated, such as using atlas-based segmentation, creating plans, setting dose grid resolution, adding beams and optimization functions or settings. Protocols create plans automatically, which drastically reduces planning time.
Scripting in RayStation provides automation, connectivity and flexibility beyond the standard user interface. Script languages IronPython and CPython give users access to all the operating system capabilities and applications such as file writing, process initiation, communication with other computers, and controlling scriptable applications such as Microsoft Office or .NET.
Clinic-specific procedures can be automated through scripting. Scripts can check for properties in a plan, such as small segments, disconnected target volumes, hotspots and undesirable gantry and couch angles. The system can then display a warning message or create a report.
Scripting provides a way to customize the interaction between RayStation and other systems for scenarios where DICOM is insufficient.
Scripting enables users to harness the power of RayStation in the way that best serves the needs of their facility. It can be used to create functionality that is not specifically available in the standard interface, such as automatic marker detection, exportation of images of non-standard dose planes, and images of all control points can be utilized as desired.
Clinics get access to data in RayStation using the powerful scripting capabilities and can find any information about single or multiple patients to reduce research times.
Learn what automation means to dosimetrists and physicists and discover advanced tools in treatment planning in this webinar from RaySearch. You’ll hear about the use of protocols, templates and scripting, automatic breast planning, fallback planning, multi-criteria optimization, the reduction of organs at risk and plan exploration.
“This automated self-check before clinical evaluation increases safety and plan homogeneity and consistency, as well saving 20 minutes per patient,” Walker says. “It’s also valuable as a training tool. I’m impressed by how much can be achieved with scripting in RayStation and how customizable the system is. We now use scripting for many aspects of the plan production process.”
Chris Walker
Head of Radiotherapy Physics, NCCC
“Being able to use RayStation programmatically like this, with the built-in scripting feature, we are able to replace a surprisingly large number of manual tasks with automation. By making just a few selections in our scripts, everything is set up and, in many cases, we can get a decently optimized treatment plan from the get-go. This feels like having a flying start to the treatment planning process.”
Christoffer Lervåg
Medical Physicist at Ålesund Hospital
The automated breast planning solution in RayStation is the first step in our journey to automate routine and standard procedures. RayAutoBreast was initially developed in Toronto, Canada. Between 2009 and 2012, clinicians at Princess Margaret Hospital (PMH) ran a large-scale clinical study to evaluate the performance of automated treatment planning for intensity modulated radiation therapy (IMRT) for tangential breast treatments. They concluded that it could add tremendous degrees of efficiency and standardization, as well as quality to current treatment planning processes. The use of automated procedures will allow for faster adoption of IMRT together with increased access to care improvements for breast cancer patients [1].
RayAutoBreast provides tools for automated generation of tangential breast IMRT plans using heuristic optimization and includes features such as:
Your data is handled with discretion, read our privacy statement
Fallback planning is a tool for creating additional plans to be used in a contingency situation, enabling a patient to be treated on another machine, possibly with a different modality and/or treatment technique, in case the original machine is unavailable. It can drastically reduce planning time in emergency situations when a machine is down allowing the patients´ treatment to continue and reducing stress on staff.
Plan explorer is based on the capability to automatically generate a large number of treatment plans for defined clinical goals and combinations of treatment techniques and machines. It also provides efficient means to filter and browse among plan candidates to find the most desired one. In addition, the plan candidates can be generated using machine learning models which also enables automatically generating several plans from one model with different tradeoff priorities.
Plan Explorer brings many potential clinical benefits and now you can:
explore more of the solution space to ensure that every radiation treatment is delivered with the highest possible efficiency, with an optimal combination of treatment technique and machine;
maximize the use of your current treatment delivery machines;
generate plan candidates using machine learning models;
explore treatment plan tradeoffs;
and get more time to evaluate the plans
What if you could deliver every treatment with an optimal combination of treatment technique and machine, for every patient, every day?
Part 1: Freddie Cardel outlines the concept of automatic plan generation with Plan Explorer and demonstrates a completely new level of automation. He explains the approach, where large numbers of high-quality treatment plans are automatically generated for defined clinical goals and combinations of treatment techniques and machines and shows how these plans can be easily filtered and browsed to find the most suitable candidates to be evaluated.
Part 2: Erik Korevaar and Roel Kierkels present the first findings of the clinical evaluation they performed at University Medical Center Groningen, the Netherlands, and describe how the center envisions the use of the tool in clinical practice.
