Classification of high-grade glioblastoma and single brain metastases using a new SCAT-inception model trained with MRI images.

Background And Objectives: Glioblastoma (GBM) and brain metastasis (MET) are the two most common intracranial tumors. However, the different pathogenesis of the two tumors leads to completely different treatment options. In terms of magnetic resonance imaging (MRI), GBM and MET are extremely similar, which makes differentiation by imaging extremely challenging.

Prediction of intraoperative cerebrospinal fluid leaks in endoscopic endonasal transsphenoidal pituitary surgery based on a deep neural network model trained with MRI images: a pilot study.

Objective: This study aimed to investigate the reliability of a deep neural network (DNN) model trained only on contrast-enhanced T1 (T1CE) images for predicting intraoperative cerebrospinal fluid (ioCSF) leaks in endoscopic transsphenoidal surgery (EETS).

Methods: 396 pituitary adenoma (PA) cases were reviewed, only primary PAs with Hardy suprasellar Stages A, B, and C were included in this study. The T1CE images of these patients were collected, and sagittal and coronal T1CE slices were selected for training the DNN model.

Resection of Insular Glioma Through the Transfrontal Limiting Sulcus Approach.

Background: The current transsylvian or transopercular approaches make access difficult because of the limited exposure of insular tumors. Hence, maximal and safe removal of insular gliomas is challenging. In this article, a new approach to resect insular gliomas is presented.

Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study.

Objectives: Ki67 is an important biomarker of pituitary adenoma (PA) aggressiveness. In this study, PA invasion of surrounding structures is investigated and deep learning (DL) models are established for preoperative prediction of Ki67 labeling index (Ki67LI) status using conventional magnetic resonance (MR) images.

Methods: We reviewed 362 consecutive patients with PAs who underwent endoscopic transsphenoidal surgery, of which 246 patients with primary PA are selected for PA invasion analysis.

Real-time augmented reality application in presurgical planning and lesion scalp localization by a smartphone.

Objective: A smartphone augmented reality (AR) application (app) was explored for clinical use in presurgical planning and lesion scalp localization.

Methods: We programmed an AR App on a smartphone. The accuracy of the AR app was tested on a 3D-printed head model, using the Euclidean distance of displacement of virtual objects.

A Supplemental Technique for Preoperative Evaluation of Giant Intracranial Aneurysm.

Background:  Preoperative planning mainly relies on digital subtraction angiography (DSA) and computed tomography angiography. However, neither technique can reveal thrombi in giant intracranial aneurysms (GIAs). In this study, we aimed to reconstruct the circulating and noncirculating parts of GIAs with the time-of-flight (TOF) and motion-sensitized driven-equilibrium (MSDE) sequences with 3D Slicer to reveal an integrated presentation of GIAs, compare its accuracy, and validate the usefulness for preoperative planning.