Deep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS).

Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset.

A method for mimicking tumour tissue in brain ex-vivo ultrasound for research application and clinical training.

Background: Intraoperative ultrasound is becoming a common tool in neurosurgery. However, effective simulation methods are limited. Current, commercial, and homemade phantoms lack replication of anatomical correctness and texture complexity of brain and tumour tissue in ultrasound images.

Intraoperative superb microvascular ultrasound imaging in glioma: novel quantitative analysis correlates with tumour grade.

Accurate grading of gliomas is critical to guide therapy and predict prognosis. The presence of microvascular proliferation is a hallmark feature of high grade gliomas which traditionally requires targeted surgical biopsy of representative tissue. Superb microvascular imaging (SMI) is a novel high resolution Doppler ultrasound technique which can uniquely define the microvascular architecture of whole tumours.

Single-cell epigenetic profiling reveals an interferon response-high program associated with deficiency in kidney cancer.

Renal cell carcinoma (RCC) is characterized by recurrent somatic mutations in epigenetic regulators, which stratify patients into clinically significant subgroups with distinct prognoses and treatment responses. However, the cell type-specific epigenetic landscape of RCC-broadly and in the context of these mutations-is incompletely understood. To investigate these open questions, we integrated single nucleus ATAC sequencing data from RCC tumors across four independent cohorts.