Radiomic-based nonlinear supervised learning classifiers on non-contrast CT to predict functional prognosis in patients with spontaneous intracerebral hematoma.

PURPOSE: To evaluate if nonlinear supervised learning classifiers based on non-contrast CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma. METHODS: Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE > 18 years and with TCCSC performed within the first 24 h of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS 0-2) and poor prognosis (mRS 3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30% respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort. RESULTS: 105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC 0.798, 0.752 and 0.742 respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (0.778-1;95%CI), with a false-negative rate of 0% for predicting poor functional prognosis at discharge. CONCLUSION: The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.

Usefulness of CT angiography for characterizing cerebral arteriovenous malformations presenting as hemorrhage: comparison with digital subtraction angiography. / Utilidad de la angio-TC para la caracterización de malformaciones arteriovenosas cerebrales con presentación hemorrágica comparada con la angiografía por sustracción digital.

OBJECTIVE: To compare the usefulness of CT angiography against the gold standard, digital subtraction angiography (DSA), in the characterization of cerebral arteriovenous malformations (AVM) that present with bleeding. MATERIAL AND METHODS: We retrospectively analyzed patients with intracranial bleeding due to an AVM who were included in a prospective database in the period comprising January 2007 through December 2012. We reviewed radiologic variables such as the characteristics of the AVM (size, location, presence of deep venous drainage), involvement of eloquent areas, and the presence of associated aneurysms. Two neuroradiologists blinded to clinical and radiological information analyzed the CT and DSA in consensus. RESULTS: A total of 22 patients were included in the study. CT angiography correctly classified 15 of the 16 cases of AVM measuring less than 3cm (93.75% sensitivity). All cases of deep venous drainage and all those located in eloquent areas were correctly detected (100% sensitivity). The presence of any type of aneurysm related with the AVM was detected in 13 of 15 cases (86.6% sensitivity); 7 of 9 of the intranidal aneurysms were detected (77.78% sensitivity), as were 6 of the 9 flow aneurysms (66.67% sensitivity). CONCLUSION: CT angiography is highly sensitive in the characterization of cerebral AVMs measuring less than 3cm, of those located in eloquent areas, and of those with deep venous drainage; it is also highly sensitive in detecting aneurysms related with AVMs. However, CT angiography is less sensitive in detecting intranidal and flow aneurysms related with AVMs.