ArticleInternational journal of particle therapy2026
Machine Learning-Based Beam Delivery Time Model for Mevion S250i With Hyperscan Technology.
Article in International journal of particle therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Interplay effect evaluation on lung and esophageal cancer patients treated with compact proton system in free breathing.Physics and imaging in radiation oncology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Accurate prediction of beam delivery time (BDT) is critical for operational efficiency, 4D dose calculations, and advanced proton therapy techniques. Despite its importance, no machine-specific BDT model exists for Mevion systems. Methods: We developed the first machine learning-based model for the Mevion S250i Hyperscan system. Institutional machine log files from 11 patients (1120 machine log files) were used to extract features describing spot position, energy layer changes, Adaptive Aperture (AA) movements, and spot charge. Inter-pulse time ( Results: The model achieved mean absolute errors (MAE) ranging from 0.9 ms for short intervals ( Conclusions: This study presents the first machine learning-based BDT model for the Mevion S250i system, accurately capturing both predictive performance and machine-specific temporal dynamics. Explainable AI analysis using SHAP provided detailed insights into the operational characteristics of the system, highlighting the contributions of energy layer switching, AA adjustments, and spot position shifts to delivery time. The proposed BDT model demonstrated strong predictive performance across the clinical applications evaluated, supporting its potential use for interplay assessment, 4D dose calculation, and delivery time-based plan optimization.
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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 Socratic graph.