Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations

Eric Karl Oermann, Alex Rubinsteyn, Dale Ding, Justin Mascitelli, Robert M. Starke, Joshua B. Bederson, Hideyuki Kano, L. Dade Lunsford, Jason P. Sheehan, Jeffrey Hammerbacher, Douglas Kondziolka

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

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.

Original languageEnglish (US)
Article number21161
JournalScientific reports
Volume6
DOIs
StatePublished - Feb 9 2016
Externally publishedYes

ASJC Scopus subject areas

  • General

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