ArticleMedical physics2026
Evaluating the role of plan complexity metrics in online adaptive radiotherapy for pancreatic cancer patients.
Article in Medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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10 authors.
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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 (σ
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.
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