Abstract WP423: Advanced Machine Learning in Action: Radiomic Based Outcome Prediction of Acute Intracranial Hemorrhage on Computed Tomography

Abstract

Background: Intracranial hemorrhage (ICH) requires prompt diagnosis to optimize patient outcomes 1 . We hypothesized that machine learning algorithms could automatically analyze non-contrast computed tomography (NECT) of the head and predict clinical outcome of ICH patients 2 . Methods: 300 NECTs with acute spontaneous ICH between 2014-2019 were retrospectively included from the database at a tertiary university hospital. A binary outcome was defined as Modified Ranking Scale (mRS) 0-3 (good outcome) and mRS 4-6 (bad outcome) at discharge. Radiomic features including shape, histogram and texture markers were extracted from non- , wavelet- and log-sigma-filtered images using regions of interest of ICH. The quantitative predictors were evaluated utilizing random forest algorithms with 5-fold model-external cross-validation. Results: The model achieved an area under the ROC curve of 0.81 (95% CI [0.077; 0.86]; P<0.01), specificities and sensitivities reached 78% at Youden’s Index optimal cut-off point for the prediction of functional clinical outcome at discharge (mRS). Discussion: In conclusion, quantitative features of acute NECT images in a machine learning algorithm provided high discriminatory power in predicting functional outcome. In clinical routine, this proposed approach could allow early triage of high-risk patients for poor outcome. Indication of source:1 Qureshi, A. I. et al. Intracerebral haemorrhage. Lancet. 2009. 2 Mohammad R. Arbabshirani et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Medicine. 2018.