ArticleJournal of applied clinical medical physics2021
Knowledge-based planning in robotic intracranial stereotactic radiosurgery treatments.
Article in Journal of applied clinical medical physics, 2021. 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.
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Who cites it
1 citing paper in PubMed.
- Development and validation of a knowledge-based model for robotic radiosurgery planning for brain lesions.Physics and imaging in radiation oncology · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a knowledge-based planning (KBP) model that predicts dosimetric indices and facilitates planning in CyberKnife intracranial stereotactic radiosurgery/radiotherapy (SRS/SRT).
methodsForty CyberKnife SRS/SRT plans were retrospectively used to build a linear KBP model which correlated the equivalent radius of the PTV (r
resultsOur KBP model showed desirable predictability. For the 40 training plans, the average prediction error from cross-validation was only 0.36 ± 0.06 mm for ΔGM, and 0.12 ± 0.08 for ΔV
conclusionWe demonstrated a linear KBP based on PTV volumes that accurately predicts CyberKnife SRS/SRT planning dosimetric indices and greatly helps achieve superior plan quality and planning efficiency.
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