Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI Using Noisy Student-Based Training.

The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. The authors previously developed a framework for detecting small BM (with diameters of <15 mm) in T1-weighted contrast-enhanced 3D magnetic resonance images (T1c). This study aimed to advance the framework with a noisy-student-based self-training strategy to use a large corpus of unlabeled T1c data.

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI.

Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural similarities to normal structures (e.

Retrieval characteristics of the Bard Denali and Argon Option inferior vena cava filters.

Objective: The purpose of this study was to compare the retrieval characteristics of the Option Elite (Argon Medical, Plano, Tex) and Denali (Bard, Tempe, Ariz) retrievable inferior vena cava filters (IVCFs), two filters that share a similar conical design.

Methods: A single-center, retrospective study reviewed all Option and Denali IVCF removals during a 36-month period. Attempted retrievals were classified as advanced if the routine "snare and sheath" technique was initially unsuccessful despite multiple attempts or an alternative endovascular maneuver or access site was used.

Radiology and Enterprise Medical Imaging Extensions (REMIX).

Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of "big imaging data," as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence.

Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.

Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non-contrast material-enhanced head computed tomographic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI). Materials and Methods This HIPAA-compliant retrospective study was completed after institutional review board approval. A training and validation dataset of noncontrast-enhanced head CT examinations that comprised 100 examinations of HMH, 22 of SAI, and 124 of noncritical findings was obtained resulting in 2583 representative images.

Genetic validation of the protein arginine methyltransferase PRMT5 as a candidate therapeutic target in glioblastoma.

Glioblastoma is the most common and aggressive histologic subtype of brain cancer with poor outcomes and limited treatment options. Here, we report the selective overexpression of the protein arginine methyltransferase PRMT5 as a novel candidate theranostic target in this disease. PRMT5 silences the transcription of regulatory genes by catalyzing symmetric dimethylation of arginine residues on histone tails.