Abstract WMP101: Prediction of Clinical Outcome in Supratentorial Intracerebral Hemorrhage: Application of Baseline Ct Scan Radiomics Feature Extraction and Machine Learning Classifiers

Abstract

Aim: Radiomics refers to automatic extraction of numerous quantitative features from medical images to supplement visual assessment. Machine-learning algorithms provide a suitable statistical methodology for devising predictive classifiers based on large radiomics datasets. We aimed to predict intracerebral hemorrhage (ICH) outcome by applying machine-learning classifiers to both clinical data and hematoma radiomics features. Methods: Patients enrolled in the Yale Longitudinal Study of ICH were included if they had (1) spontaneous supratentorial ICH, (2) baseline CT scan, (3) known admission Glasgow Coma Scale (GCS), and (4) 3-month modified Rankin Scale (mRS). A total of 1134 radiomics features related to the intensity, shape, texture, and waveform were extracted from manually segmented ICH lesions on baseline CT. Clinical variables were patients’ age, gender, GCS, presence of intraventricular hemorrhage, and thalamic ICH. We calculated the averaged receiver operating characteristics (ROC) area under curve (AUC) in outcome prediction among 100 repeats of 5-fold cross-validation (x500 iterations) for different combinations of feature selection and machine-learning algorithms. Results: A total of 119 ICH patients were included, of whom 60 had poor outcome (mRS ≥4). Among different combinations, lasso regression feature selection and partial least square (PLS) classification model yielded the highest accuracy in outcome prediction (Figure), with an averaged (95% confidence interval) ROC AUC of 0.86 (0.83 - 0.89) using clinical variables “only”, versus 0.92 (0.89 - 0.95) using combination of clinical variables and 54 radiomics features selected by lasso regression. Among radiomics features selected by lasso regression, ICH lesion flatness had the highest variable importance and was the only shape feature selected. Conclusion: Addition of ICH lesion radiomics to clinical variables using machine-learning models can improve outcome prediction.