ReviewJournal of neuro-oncology2026
Artificial intelligence in gamma knife radiosurgery: applications in treatment planning, response prediction, and toxicity assessment.
Review in Journal of neuro-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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial intelligence (AI) is increasingly being developed across the Gamma Knife radiosurgery (GKRS) workflow, but its current applications and readiness for clinical integration remain underutilized. This narrative review examines the emerging role of AI in GKRS, with emphasis on treatment planning, outcome prediction, and post-treatment assessment.
methodsA structured search of PubMed/MEDLINE, Embase, and Scopus was conducted through September 20, 2026 to identify studies applying learning-based AI approaches specifically to GKRS. Eligible studies evaluated AI models using GKRS clinical, imaging, or treatment-planning data, while non-GKRS and non-learning-based studies were excluded. Included studies were synthesized according to major stages of the GKRS workflow.
resultsAI applications were identified across tumor segmentation, isocenter selection and treatment planning, prescription and dose-distribution prediction, synthetic computed tomography generation, and plan-quality assessment. Post-treatment applications include longitudinal tumor monitoring and prediction of response, functional outcomes, and toxicity including peritumoral edema and radiation necrosis. Several models demonstrate promising performance, but the evidence remains predominantly retrospective and task-specific, with frequent reliance on single-center or small datasets and limited external or prospective validation.
conclusionAI has the potential to improve the consistency, efficiency, and personalization of GKRS by supporting specific technical and clinical decisions. Further multicenter validation, prospective evaluation, workflow integration, and demonstration of meaningful clinical benefit are required before routine adoption. AI currently remains a decision-support tool rather than a replacement for radiosurgical expertise.
Indexed as
Identifiers
42905327What 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.