Evaluating the role of plan complexity metrics in online adaptive radiotherapy for pancreatic cancer patients.

Abstract

PurposeTo systematically investigate the behavior of plan complexity metrics (PCMs) in an MR-Linac online adaptive radiotherapy (oART) workflow for pancreatic cancer, and to evaluate their potential as surrogate indicators of delivery accuracy.MethodsThirty-seven patients with locally advanced pancreatic cancer were retrospectively analyzed, yielding 222 MR-Linac plans (37 reference and 185 delivered fractions). Fifteen PCMs were extracted from plans generated with three optimizers: Penalty, Objectives and Constraints, and A3i (current clinical practice). Plan specific quality assurance (PSQA) has been performed through an independent dose calculation algorithm. Statistical analyses included: (i) inter-optimizer comparisons (ANOVA and mixed-effects models), (ii) variance decomposition of adapted-plan complexity metrics using linear mixed-effects models (LMEMs), and (iii) evaluation of PSQA stability using statistical process control (SPC) and leave-one-patient-out (LOPO) cross-validation.ResultsOptimizer choice strongly influenced plan complexity. The Penalty optimizer generated higher-complexity plans, whereas Objectives and Constraints and A3i produced more modulation-efficient configurations with fewer small, low-MU segments. Variance decomposition identified a subset of metrics that exhibited consistent behavior across all optimizers, serving as robust descriptors independent of the algorithm. Metrics dominated by between-patient variance (σ² between) emerged as reliable surrogates for patient-specific complexity Tongue & Groove Index, Average Leaf Gap and Number of Active Leaves consistently showed high between-patient contributions (σ² between > 74%) among others. In contrast, metrics related to low-MU segments (SegMU MU within), with A3i showing the most pronounced fluctuations (85.8% and 84.8%, respectively). These descriptors are therefore more sensitive to plan-specific or optimizer-related stochasticity than to stable patient factors. SPC analyses demonstrated that the current adaptive workflow is robustly stable for most patients: 11 of 13 never experienced a fraction below the tolerance level (TL), and 12 of 13 never exceeded the action level (AL), even when thresholds were dynamically recalculated within the LOPO-CV.ConclusionThis study provides the first systematic assessment of PCMs in MR-guided oART, demonstrating optimizer-specific complexity signatures, predominant inter-patient variability, and the predictive value of selected metrics for delivery accuracy. Although limited to a single tumor site and workflow, the methodology supports the development of institution-specific, complexity-aware scorecards to enhance adaptive planning and quality assurance.