Automatic feature extraction and machine learning prediction of stroke functional outcome based on histogram information of baseline ADC

This study demonstrates an automatic method that predicts favorable/unfavorable clinical outcome based on pre-treatment DWI data and machine learning (ML) in acute ischemic stroke. We present the use of ADC histogram information in the brain tissue as features for the ML prediction. In the histogram analysis, the 5 or 10 percentile value of the ADC distribution was indicative of clinical outcome regardless of success/failure of recanalization. The ROC analysis in unseen test subjects resulted in an area under the curve (AUC) of 0.79 with the proposed feature extraction, which was greater than 0.71 with the DWI lesion volume only.