ReviewJournal of neuro-oncology2023
Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.
Review in Journal of neuro-oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis.Neurosurgical review · 2024Pooled it
- Gamma knife radiosurgery for cerebellar brain metastases: clinical outcomes and artificial intelligence-based predictive modeling.Clinical & experimental metastasis · 2026Article
- Integrating artificial intelligence with Gamma Knife radiosurgery in treating meningiomas and schwannomas: a review.Neurosurgical review · 2025Review
- Establishment and evaluation of an automatic multi?sequence MRI segmentation model of primary central nervous system lymphoma based on the nnU?Net deep learning network method.Oncology letters · 2025Article
- Artificial intelligence innovations in neurosurgical oncology: a narrative review.Journal of neuro-oncology · 2024Review
- Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants.Diagnostics (Basel, Switzerland) · 2023Article
- Is it advisable to perform radiosurgery for EGFR-TKI-controlled brain metastases? A retrospective study of the role of radiosurgery in lung cancer treatment.Journal of neuro-oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery.
methodsLiteratures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed.
resultsAI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application.
conclusionsStereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.
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.