Assessment of Knowledge-Based Planning Model in Combination with Multi-Criteria Optimization in Head-and-Neck Cancers

AIM: The aim of this study was to build knowledge-based planning model (KBPM) for head-and-neck (HN) cancers using volumetric-modulated arc therapy (VMAT), optimized with multi-criteria optimization (MCO), and to evaluate KBPM plan quality with clinical plan (CP) using in-house developed Python script. MATERIALS AND METHODS: Two hundred previously treated simultaneously integrated boost (SIB) HN VMAT plans (RapidArc®) were selected for creating KBPM. These plans were further optimized using MCO to strike right trade-off between target and organs at risk (OARs). The script was written using Python V3.7.1 to automatically extract and analyze treatment plan dosimetric parameters through Eclipse Scripting Application Programming Interface (ESAPI). Analyzed plans that met deliverable quality were modeled using regression-based KBPM framework. The trained model is validated with 35 cohorts of HN SIB patients. RESULTS: MCO plans were able to improve the OAR sparing without compromising target coverage compared to user-optimized CPs except for increased heterogeneity. With MCO, spinal cord dose D0.03cc is reduced by 3.2 Gy ± 1.8 Gy, parotid mean dose by 2 Gy ± 1.7 Gy compared to CPs, respectively. MCO-based KBPM plans were comparable to CP with improved sparing for left and right parotids by 11.5% and 7.8%, respectively. CONCLUSION: MCO-based KBPM plans were superior to user plans in terms of OAR sparing and user need to spend more time to meet the model-based plan outcomes. Created KBPM planning is simple and efficient to generate estimate for OAR sparing to guide entry and intermittent planners to improve their clinical planning skills with lesser planning time. Python ESAPI is a powerful tool to extract plan parameters and quickly evaluate either individual or a cohort of plans.