A simple parameter to guide plan optimization for robotic pancreas SBRT.

Abstract

Pancreatic SBRT planning is challenging due to the proximity of highly sensitive organs at risk. Since automated planning is currently not available for the CyberKnife (CK) system, the optimization of high-quality treatment plans relies heavily on expert planners; however, even in this setting, the generation of suboptimal plans remains possible. The aim of this work was to develop a tool to guide plan optimization based on simple geometric parameters and institutional planning experience. As a first step, the predictive value of the Expansion-Intersection Volume (EIV), defined as the intersection volume between the PTV expanded by 5 mm and the duodenum, stomach, and bowel, was evaluated together with GTV and PTV volumes. The investigated outputs included PTVV40Gy [%], GTVV47.5 Gy [%], GTVV50Gy [%], monitor units (MUs), delivery time, and a plan complexity score. A machine learning-based tool was then developed to predict these outputs for new patients using information extracted from 41 previously optimized plans, while also identifying the most similar historical cases. A moderate-to-strong significant correlation was observed between the three input parameters and the three dosimetric outputs. A similar relationship was found for the remaining plan efficiency and complexity metrics. The tool was tested on 10 new patient plans, and predicted values were compared with the corresponding software-guided planning results. A simple knowledge-based planning-support tool was developed. Using only three input parameters (EIV, PTV volume, and GTV volume), the tool provides an estimate of achievable target coverage, plan efficiency, and complexity for CK pancreatic SBRT. In addition, it identifies similar historical cases, providing a useful starting point for plan optimization and helping to reduce the risk of suboptimal planning outcomes.