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Assessment of Knowledge-Based Planning Model in Combination with Multi-Criteria Optimization in Head-and-Neck Cancers
Pichandi Anchineyan1, Jerrin Amalraj1, Bijina Themantavida Krishnan1
1Department of Radiation Oncology, Healthcare Global Enterprises, Bengaluru, Karnataka, India.
Journal of Medical Physics
|October 10, 2022
Summary
A new knowledge-based planning model (KBPM) for head-and-neck cancers using VMAT and MCO improves organ sparing. This model, developed with Python, offers efficient planning and enhances clinical skills for radiation oncologists.
Area of Science:
- Radiation Oncology
- Medical Physics
- Cancer Treatment Planning
Background:
- Volumetric-modulated arc therapy (VMAT) is a standard for head-and-neck (HN) cancer treatment.
- Multi-criteria optimization (MCO) aims to balance target coverage and organ-at-risk (OAR) sparing.
- Knowledge-based planning models (KBPM) can streamline treatment planning.
Purpose of the Study:
- To develop a KBPM for HN cancers using VMAT and MCO.
- To evaluate the quality of KBPM-generated plans against clinical plans (CP).
- To assess the efficiency and utility of a Python script for plan analysis.
Main Methods:
- A KBPM was created using 200 HN VMAT plans optimized with MCO.
- A Python script (V3.7.1) utilizing Eclipse Scripting Application Programming Interface (ESAPI) was developed for data extraction and analysis.
- The regression-based KBPM was trained and validated with 35 patient cohorts.
Main Results:
- MCO improved OAR sparing (e.g., spinal cord, parotids) compared to CPs, with minimal impact on target coverage.
- KBPM plans showed comparable or superior OAR sparing to CPs.
- Python ESAPI facilitated efficient plan parameter extraction and evaluation.
Conclusions:
- MCO-based KBPM plans are superior to user-optimized plans for OAR sparing.
- The developed KBPM provides an efficient method for estimating OAR sparing, aiding planner skill development.
- Python ESAPI is a valuable tool for evaluating radiation treatment plans.
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