Trial reportScientific reports2016
Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations.
Trial report in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses 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
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiation-induced malignancies after stereotactic radiosurgery for brain arteriovenous malformations: a large single-center retrospective study and systematic review.Neurosurgical review · 2024Pooled it
- Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.Neurosurgical review · 2020Pooled it
- Review
- Neuro Data Hub: A New Approach for Streamlining Medical Clinical Research.Neurosurgery practice · 2025Article
- Review
- DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery.AJNR. American journal of neuroradiology · 2024Article
- Artificial intelligence in the surgical management of arteriovenous malformations.Neurosurgical review · 2024Article
- Machine Learning for Individualized Risk Estimation in Anterior Lumbar Interbody Fusion.Neurosurgery practice · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.BMC musculoskeletal disorders · 2024Article
- Article
- Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction.Journal of imaging informatics in medicine · 2024Article
- Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs.JMIR neurotechnology · 2024Article
- Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS).Diagnostics (Basel, Switzerland) · 2023Article
- A machine learning approach to predict the glaucoma filtration surgery outcome.Scientific reports · 2023Article
- Using Machine Learning to Predict Nonachievement of Clinically Significant Outcomes After Rotator Cuff Repair.Orthopaedic journal of sports medicine · 2023Article
- Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study.Quantitative imaging in medicine and surgery · 2023Article
- Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence.Brain sciences · 2023Review
- Article
- Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.Frontiers in human neuroscience · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predictions of patient outcomes after a given therapy are fundamental to medical practice. We employ a machine learning approach towards predicting the outcomes after stereotactic radiosurgery for cerebral arteriovenous malformations (AVMs). Using three prospective databases, a machine learning approach of feature engineering and model optimization was implemented to create the most accurate predictor of AVM outcomes. Existing prognostic systems were scored for purposes of comparison. The final predictor was secondarily validated on an independent site's dataset not utilized for initial construction. Out of 1,810 patients, 1,674 to 1,291 patients depending upon time threshold, with 23 features were included for analysis and divided into training and validation sets. The best predictor had an average area under the curve (AUC) of 0.71 compared to existing clinical systems of 0.63 across all time points. On the heldout dataset, the predictor had an accuracy of around 0.74 at across all time thresholds with a specificity and sensitivity of 62% and 85% respectively. This machine learning approach was able to provide the best possible predictions of AVM radiosurgery outcomes of any method to date, identify a novel radiobiological feature (3D surface dose), and demonstrate a paradigm for further development of prognostic tools in medical care.
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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 OpenQuestion graph.