title: Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations.
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.
authors: Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
authors: Eric Karl
authors: EK
authors: Oermann
authors: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
authors: Alex
authors: A
authors: Rubinsteyn
authors: Department of Neurosurgery, University of Virginia Medical Center, Charlottesville, VA, USA.
authors: Dale
authors: D
authors: Ding
authors: Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
authors: Justin
authors: J
authors: Mascitelli
authors: Department of Neurosurgery, University of Virginia Medical Center, Charlottesville, VA, USA.
authors: Robert M
authors: RM
authors: Starke
authors: Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
authors: Joshua B
authors: JB
authors: Bederson
authors: Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
authors: Hideyuki
authors: H
authors: Kano
authors: Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
authors: L Dade
authors: LD
authors: Lunsford
authors: Department of Neurosurgery, University of Virginia Medical Center, Charlottesville, VA, USA.
authors: Jason P
authors: JP
authors: Sheehan
authors: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
authors: Jeffrey
authors: J
authors: Hammerbacher
authors: Department of Neurosurgery, New York University Langone Medical Center, New York City, NY, USA.
authors: Douglas
authors: D
authors: Kondziolka
mesh_terms: D005260:Female; D006801:Humans; D002538:Intracranial Arteriovenous Malformations / Q000401:mortality* / Q000532:radiotherapy*; D000069550:Machine Learning; D008297:Male; D011237:Predictive Value of Tests; D016634:Radiosurgery