Radiation Planning Assistant (RPA)
Overview
The Radiation Planning Assistant (RPA) is a web-based artificial intelligence (AI)-assisted tool developed by Laurence Court, Ph.D., and his team at the University of Texas MD Anderson Cancer Center. It offers automated contouring and planning solutions for various radiotherapy indications, primarily designed to improve access to high-quality radiotherapy in low- and middle-income countries (LMICs) where there is a shortage of skilled specialists.
The RPA streamlines the multi-step process of radiation planning, which traditionally involves clinicians analyzing medical images to design personalized treatment plans, including targeting radiation, avoiding specific areas, and determining dosage. The platform allows clinicians to upload a CT scan and complete a service request form. The de-identified data is then processed on RPA servers.
For complex cases, the system first performs automatic contouring, which is then reviewed and finalized by the clinician before the system delivers the final treatment plan. For simpler cases, the plan is automatically created without this intermediate review step. The RPA is powered by AI tools for preparation and contouring tasks, with plan optimization and dose calculation automatically performed in conjunction with the Eclipse Treatment Planning System (Varian Medical Systems). It also incorporates quality assurance checks to flag suspicious contours or plans for user investigation.
Once the plan is generated, it is sent back to the clinician for recalculation of dosages and any necessary edits before clinical utilization. The RPA is intended to improve workflows and support clinical teams, not to be used as a primary treatment planning system, with final evaluation and approval remaining with the user institution.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Automated contouring of normal tissues and clinical target volumes
- Automated radiotherapy treatment planning (simple and advanced, e.g., 4-field box, VMAT)
- AI-powered dose optimization
- Integrated quality assurance checks to flag suspicious plans
- Web-based platform for accessibility
- De-identification of patient data upon upload
- Supports both one-step (fully automated) and two-step (user-reviewed contouring) planning workflows
- Integration with commercial treatment planning systems (e.g., Varian Eclipse) via API
- Designed for use in resource-limited settings
Use Cases
- Streamlining radiation therapy planning workflows
- Increasing access to high-quality radiotherapy in low- and middle-income countries (LMICs)
- Automated planning for head and neck cancer (VMAT)
- Automated planning for cervical cancer (4-field box, VMAT)
- Automated planning for breast cancer (chest wall, tangents)
- Automated planning for brain metastases (whole brain)
What Physicians Need to Know
Leverage the RPA to significantly reduce routine planning time, allowing for greater focus on complex cases, patient interaction, and professional development. Always perform a thorough review and make necessary edits to the AI-generated contours and plans within your local TPS, as the RPA is an assistant tool, not a replacement for expert clinical judgment. Participate in the ongoing evaluation and feedback mechanisms to contribute to the tool's continuous improvement, especially regarding its applicability to diverse patient populations and clinical practices.
The RPA is a web-based platform that requires seamless data transfer (CT scans) and integration with your existing local Treatment Planning System (TPS), specifically Varian's Eclipse, for dose calculation and final plan validation. Ensure your local TPS is audited and compatible, and plan for a workflow that incorporates downloading, reviewing, editing, and recalculating doses for all RPA-generated plans before clinical use.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing |
Freemium / tiered
|
| Deployment | Cloud-based / Web-based |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Yes AI-estimated |
| FDA Status |
1 AI-estimated The Radiation Planning Assistant (RPA) received FDA 510(k) clearance in 2023. It is cleared for planning radiotherapy treatments for patients with cancers of the head and neck, cervix, breast, and metastases to the brain. |
| Integrations | |
| EHR | Not specified |
| Specialties | Oncology |
What the Web Says
The Radiation Planning Assistant (RPA) is a web-based, AI-powered tool developed by MD Anderson Cancer Center to automate radiotherapy contouring and planning, primarily for low- and middle-income countries (LMICs). It aims to address the global shortage of skilled radiotherapy professionals and improve access to high-quality cancer care by streamlining the treatment planning process. Physicians globally have reviewed RPA-generated plans, finding them largely acceptable as-is or with minor edits, indicating clinical relevance and safety.
Overall: PositiveStrengths
- Automates radiotherapy contouring and planning, reducing planning time and workload.
- Increases access to high-quality radiotherapy in low-resource settings.
- Provides consistent and standardized treatment plans, potentially improving quality and reducing variability.
- Web-based and equipment-agnostic, making it accessible across diverse clinical environments.
- Clinically validated with high rates of physician acceptability for various cancer types.
- Designed to be offered at no cost to clinics in LMICs.
Limitations
- Requires final review, editing, and approval by qualified clinical staff.
- Some plans may require minor edits or may not be immediately usable as-is for certain approaches or cancer types.
- The user is not able to easily edit RPA VMAT plans; if not approved, a new plan must be created using local systems.
- Initial studies showed mixed responses regarding benefits to consistency among providers.
- The system is not intended to be used as a primary treatment planning system.
- While automation offers efficiency, a significant impact on the time between CT imaging and treatment initiation has yet to be definitively shown.
Based on reviews from: ASCO Publications, Cancerworld, DigitalCommons@TMC, PMC, MD Anderson Cancer Center, UCL, accessdata.fda.gov, Imaging Technology News, AAPM, PubMed, Capterra, Reddit, G2
Last updated: 2026-07-19
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Radiotherapy Treatment Planning System: Radiation Planning Assistant | Protocol Preview
JoVE (Journal of Visualized Experiments)
