Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.

Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.

Radiomic Consensus Clustering in Glioblastoma and Association with Gene Expression Profiles.

Background/objectives: Glioblastoma (GBM) is the most common malignant primary central nervous system tumor with extremely poor prognosis and survival outcomes. Non-invasive methods like radiomic feature extraction, which assess sub-visual imaging features, provide a potentially powerful tool for distinguishing molecular profiles across groups of patients with GBM. Using consensus clustering of MRI-based radiomic features, this study aims to investigate differential gene expression profiles based on radiomic clusters.

Meralgia Paresthetica: An Anatomical Review and Surgical Case Presentation Utilizing a Robotic Exoscope.

Background: Meralgia paresthetica (MP) is a neuropathic condition marked by pain, tingling, and numbness in the anterolateral thigh, primarily caused by compression of the lateral femoral cutaneous nerve (LFCN). Although compression often occurs beneath the inguinal ligament, anatomical variations can lead to different entrapment sites. Treatments range from conservative measures to surgical decompression, depending on symptom severity.

Midbrain volume in brain herniation: A volumetric analysis in operative traumatic brain injury.

Background: This study primarily aimed to assess the volumetric attributes of the midbrain and perimesencephalic structures preoperatively and following surgical interventions in patients diagnosed with brain herniation secondary to traumatic brain injury (TBI).

Methods: We evaluated patients based on radiological findings and clinical symptoms indicative of brain herniation. We performed semi-automated segmentation of the intracranial structures most relevant to trauma and of interest for the current study, such as hematoma, ventricles, midbrain, and perimesencephalic cisterns.