ArticleFrontiers in oncology2026
Can knowledge-based autoplanning keep up with advances in radiotherapy optimization for oropharyngeal cancer?
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The performance of knowledge-based autoplanning depends on historical plans used to train the autoplanning models. Here, we investigate the applicability of an existing RapidPlan (RP) model for head and neck cancer to a novel planning and delivery solution (RapidArc Dynamic, RAD), which has the potential to improve plan quality through the integration of new degrees of freedom. RAD integrates static-angle modulated ports and a dynamic collimator in volumetric-modulated arc therapy (VMAT) fields. Materials and methods: A cohort of 48 oropharyngeal cancer (OPC) patients was retrospectively included in the planning study. Organ-at-risk (OAR) sparing was evaluated for VMAT and RAD, respectively, using both a VMAT-based RP model and a RAD-based RP model, resulting in four plans per patient: VMAT (RP-VMAT), VMAT (RP-RAD)), RAD (RP-VMAT) and RAD (RP-RAD). Differences were assessed with the related-samples Friedman's two-way ANOVA test by ranks, correcting the p value for multiple testing (p ≤ 0.05 considered significant). Results: For RAD plans, the RAD-based RP model improved the sparing of all OAR (p ≤ 0.001) except the parotids. However, the RAD plans were at least equivalent to the VMAT plans when optimizing with RP-VMAT, indicating the safety of initially implementing RAD with a VMAT-based RP model. In addition, when optimizing with RP-RAD, all OAR except the trachea were significantly better spared with RAD (RP-RAD) compared to VMAT (RP-RAD) (p = 0.039 for the esophagus and < 0.001 for the remaining OAR), with a median reduction of Dmean by 4.8 Gy and 3.5 Gy, respectively, for the larynx and the constrictor muscle. There was also a significant reduction in the estimated risk of dysphagia (1.9 pp) and acute mucositis (1.3 pp) (p ≤ 0.001). Conclusions: VMAT-based RP models appear to remain applicable for optimization with the novel RAD solution until RAD-specific RP models are developed. Furthermore, RAD shows promise for OPC in terms of sparing of midline OARs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.