Radiomics features on non-contrast computed tomography predict early enlargement of spontaneous intracerebral hemorrhage

Abstract Objective To explore the value of radiomics features on non-contrast computed tomography (NCCT) in predicting early enlargement of spontaneous intracerebral hemorrhage (SICH). Patients and Methods 167 patients with SICH were divided into enlarged hematoma and non-enlarged hematoma groups based on the volume of hematoma on 24-h follow-up CT images > 30% and/or 6 ml of the baseline NCCT. The baseline NCCT images of all cases were imported into radiomics software to extract the radiomics features of the initial hematoma. For each case, the features with good predictability were retained after the feature-selected process; the remaining features were used to construct model with 23 algorithms one-by-one. A 5-fold method was used to cross-validate the model and repeated 5 times. The algorithm model with the highest accuracy was selected as predictive model for hematoma enlargement (HE) in SICH, its average parameters including AUC, accuracy, sensitivity, specificity, F1 score, positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR), false negative rate (FNR),and false discovery rate (FDR) were taken as evaluating indicators. Results A total of 1227 texture features of each cerebral hematoma were obtained. After the feature-selected process, 4 features (wavelet-LHL mean, wavelet-LLL _ Idm, wavelet-LLL _run length non-uniformity normalized, and wavelet-LLL _contrast) remained to construct the predictive models. Among 23 model algorithms, Linear Support Vector Classifier showed the highest accuracy (72.6%), and eventually was selected as the predictive model, its AUC, accuracy, sensitivity, specificity, F1 score, PPV, NPV, FPR, FNR, and FDR were 0.729, 0.726,0.717,0.736,0.714, 0.736, 0.741, 0.264, 0.283 and 0.264, respectively. Conclusion Radiomics features of cerebral hematoma on baseline NCCT images showed good performance in predicting HE of SICH.