Reliability of ABC/2 volumetric estimation in spontaneous intracerebral hemorrhage for hematoma expansion prediction scores.

Introduction: Prediction scores for hematoma expansion in spontaneous intracerebral hemorrhage (ICH), such as the 9-point and BRAIN scores, were developed predominantly using planimetry to measure hematoma volume. In this study, we aim to investigate whether the ABC/2 formula, which is known to overestimate hematoma volume, can be reliably used as a substitute for planimetry in these prediction scores.

Patients And Methods: A total of 429 patients from four hospitals were retrospectively enrolled.

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network.

Hematoma expansion occasionally occurs in patients with intracerebral hemorrhage (ICH), associating with poor outcome. Multimodal neural networks incorporating convolutional neural network (CNN) analysis of images and neural network analysis of tabular data are known to show promising results in prediction and classification tasks. We aimed to develop a reliable multimodal neural network model that comprehensively analyzes CT images and clinical variables to predict hematoma expansion.

Three-dimensional Images Fusion Method Useful for Preoperative Simulation of Clipping Surgery for Cerebral Aneurysms.

This study aimed to introduce a three-dimensional (3D) images fusion method for preoperative simulation of aneurysm clipping. Consecutive unruptured aneurysm cases treated with surgical clipping from March 2021 to October 2023 were included. In all cases, preoperative images of plain computed tomography (CT), CT angiography, magnetic resonance imaging (MRI) 3D fluid-attenuated inversion recovery, 3D heavily T2-weighted images, and 3D rotational angiography were acquired and transported into a commercial software (Ziostation2 Plus, Ziosoft, Inc.

Glioblastoma with concomitant moyamoya vasculopathy in neurofibromatosis type 1: illustrative case.

Background: In a case of concurrent glioblastoma and moyamoya vasculopathy, it is arduous to safely perform surgery because the brain is highly vulnerable and collaterals are sometimes well developed. In addition, radiotherapy carries a risk of aggravating moyamoya vasculopathy, and chemotherapeutic agents also have a risk of interfering with collateral development.

Observations: A 48-year-old woman with neurofibromatosis type 1 was admitted because of left hemiparesis and hemispatial neglect.

Machine learning prediction of hematoma expansion in acute intracerebral hemorrhage.

To examine whether machine learning (ML) approach can be used to predict hematoma expansion in acute intracerebral hemorrhage (ICH) with accuracy and widespread applicability, we applied ML algorithms to multicenter clinical data and CT findings on admission. Patients with acute ICH from three hospitals (n = 351) and those from another hospital (n = 71) were retrospectively assigned to the development and validation cohorts, respectively. To develop ML predictive models, the k-nearest neighbors (k-NN) algorithm, logistic regression, support vector machines (SVMs), random forests, and XGBoost were applied to the patient data in the development cohort.