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

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Abstract Purpose To 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. Methods Thirty‐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. Results Optimizer 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 (σ 2 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 (σ 2 between > 74%) among others. In contrast, metrics related to low‐MU segments (Seg MU < 5 and Seg MU < 5 [%]) were dominated by within‐patient variance (σ 2 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. Conclusion This 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.

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