Author: Prasanna Alluri, Mona Arbab, Xingzhe Li, Chang-Shiun Lin, Mu-han Lin, David D.M. Parsons, Asal Rahimi, Justin D. Visak, Narine Wandrey 👨🔬
Affiliation: UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 🌍
Purpose: Intelligence-Optimization-Engine (IOE) v1.0 relied heavily on planner expertise and patient-specific IMRT beam arrangements, requiring frequent revisions. While VMAT workflows offered potential, IOE1.0 was restrained by its rigid two-level importance system. This led to plans occasionally caused exceeding the heart dose constraints and increase frequency of planner-intervention. We hypothesize that the IOE v2.0 can enhance planning efficiency and reduce plan revisions for hypofractionated whole breast radiotherapy (26 Gy in 5 fractions). This study evaluates IOE2.0’s impact on planning efficiency and dosimetric quality through its enhanced VMAT capabilities and four-level priority ranking system.
Methods: IOE2.0 employs a four-level priority ranking (1=most important, 4=least important) to manage clinical goals, balancing competing objectives. A standardized partial-arc VMAT template was developed to streamline workflows and reduce planner dependency. Priorities included target coverage, whole breast maximum dose limits, and critical OAR sparing, with lower ranks addressing contralateral organs and additional OARs. Challenging cases, including left- and right-sided targets, were retrospectively and prospectively planned to evaluate time savings, plan revisions, and dosimetric outcomes.
Results: IOE2.0 generated high-quality VMAT plans meeting all clinical goals, achieving PTV V95% coverage >97% and maximum doses <110%. Lung (V8.0 Gy) and heart (V7.0 Gy) constraints were consistently satisfied, even in complex cases with axillary lymph node involvement. High-quality plans required no more than two template adjustments (0.7±0.9) in most cases. VMAT computation times averaged <500 seconds (Tesla T4 server), a significant efficiency improvement over IOE1.0.
Conclusion: IOE2.0 improves planning efficiency and reduces planner interventions by combining rapid VMAT optimization with a flexible multi-level priority system. This approach consistently produces high-quality pre-plans, streamlining workflows for hypofractionated whole breast radiotherapy and enabling clinical feasibility for adaptive radiotherapy.