Joeky Senders

117 Automating clinical chart review agreement increased, model performance according to the AUC increased as well. All six variables that were labelled with a near perfect interrater agreement ( κ between 0.8 and 1) were classified with an AUC above 0.95, whereas this performance was achieved for only two out of nine variables with a κ lower than 0.8. 0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate 0.0 0.2 0.4 0.6 0.8 1.0 True Positive Rate A ROC curves for laterality characteristics Left (AUC = 0.984 ± 0.017) Right (AUC = 0.973 ± 0.024) Multifocality (AUC = 0.816 ± 0.048) 0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate 0.0 0.2 0.4 0.6 0.8 1.0 True Positive Rate B ROC curves for locational characteristics Frontal lobe (AUC = 0.96 ± 0.015) Temporal lobe (AUC = 0.965 ± 0.022) Parietal lobe (AUC = 0.966 ± 0.022) Occipital lobe (AUC = 0.982 ± 0.019) Corpus callosum (AUC = 0.945 ± 0.031) 0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate 0.0 0.2 0.4 0.6 0.8 1.0 True Positive Rate C ROC curves for tumor aspect Necrosis (AUC = 0.962 ± 0.023) Cystic (AUC = 0.957 ± 0.056) Ring enhancement (AUC = 0.926 ± 0.029) Heterogenous enhancement (AUC = 0.853 ± 0.049) 0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate 0.0 0.2 0.4 0.6 0.8 1.0 True Positive Rate D ROC curves for other characteristics Hemorrhage (AUC = 0.901 ± 0.052) Edema (AUC = 0.949 ± 0.026) Mass effect (AUC = 0.899 ± 0.037) FIGURE 1. Receiver operating characteristic curves for all extracted radiological characteristics, grouped by domain including A) tumor laterality B) tumor location C) tumor aspect and D) other characteristics.