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Robot Assisted Distal Pancreatectomy with Celiac Axis Resection (DP-CAR) for Pancreatic Cancer: Surgical Planning and Technique
Published on: August 14, 2021
A simple parameter to guide plan optimization for robotic pancreas SBRT
1Medical Physics Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
Summary
A new tool helps CyberKnife pancreatic SBRT planning by predicting plan quality using simple geometric parameters. This knowledge-based system guides expert planners, reducing suboptimal treatment outcomes.
Area of Science:
- Radiation Oncology
- Medical Physics
- Cancer Treatment
Background:
- Pancreatic SBRT planning is complex due to nearby organs at risk.
- Automated planning is unavailable for CyberKnife (CK), relying on expert planners.
- Suboptimal plans are possible even with expert input.
Purpose of the Study:
- Develop a tool to guide pancreatic SBRT plan optimization.
- Utilize simple geometric parameters and institutional experience.
- Improve treatment plan quality and efficiency for CK.
Main Methods:
- Evaluated Expansion-Intersection Volume (EIV) with GTV and PTV volumes.
- Developed a machine learning tool using 41 past plans.
- Predicted dosimetric outputs, monitor units, delivery time, and plan complexity.
Main Results:
- Significant correlations found between input parameters and dosimetric outputs.
- Similar relationships observed for plan efficiency and complexity.
- Tool validated on 10 new plans, showing accurate predictions.
Conclusions:
- A knowledge-based planning-support tool for CK pancreatic SBRT was created.
- The tool estimates target coverage, plan efficiency, and complexity using EIV, PTV, and GTV volumes.
- Identifies similar historical cases to aid optimization and prevent suboptimal plans.
