SynthesisNeurosurgical review2024
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.
Synthesis in Neurosurgical review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Prediction of local failure after stereotactic radiotherapy in melanoma brain metastases using ensemble learning on clinical, dosimetric, and radiomic data.Radiation oncology (London, England) · 2026Article
- Artificial intelligence improves survival prediction in patients with brain metastases submitted to radiosurgery.Neurosurgical review · 2026Article
- Machine Learning Analysis of Retrospective Data From 503 Hospitalized Older Patients With Type 2 Diabetes to Identify Factors Associated With Cognitive Impairment.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Review
- Review
- Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data.Diagnostics (Basel, Switzerland) · 2025Article
- Survival Prediction in Brain Metastasis Patients Treated with Stereotactic Radiosurgery: A Hybrid Machine Learning Approach.Brain sciences · 2025Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, monitoring for toxicity, and integrating with other modalities. Practical tools to predict patient outcomes are also needed. Machine learning (ML) is currently used to predict treatment outcomes. We aim to investigate the accuracy of ML in predicting treatment response and local failure of brain metastasis treated with SRS.
methodsPubMed, Scopus, Web of Science (WoS), and Embase were searched until April 16th, which was repeated on October 17th, 2023 to find possible relevant papers. The study preparation adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The statistical analysis was performed by the MIDAS package of STATA v.17.
resultsA total of 17 articles were reviewed, of which seven and eleven were related to the clinical use of ML in predicting local failure and treatment response. The ML algorithms showed sensitivity and specificity of 0.89 (95% CI: 0.84-0.93) and 0.87 (95% CI: 0.81-0.92) for predicting treatment response. The positive likelihood ratio was 7.1 (95% CI: 4.5-11.1), the negative likelihood ratio was 0.13 (95% CI: 0.08-0.19), and the diagnostic odds ratio was 56 (95% CI: 25-125). Moreover, the pooled estimates for sensitivity and specificity of ML algorithms for predicting local failure were 0.93 (95% CI: 0.76-0.98) and 0.80 (95% CI: 0.53-0.94). The positive likelihood ratio was 4.7 (95% CI: 1.6-14.0), the negative likelihood ratio was 0.09 (95% CI: 0.02-0.39), and the diagnostic odds ratio was 53 (95% CI: 5-606).
conclusionML holds promise in predicting treatment response and local failure in brain metastasis patients receiving SRS. However, further studies and improvements in the treatment process can refine the models and effectively integrate them into clinical practice.
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Registered trials
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