Deep-learning methods for auto-segmenting brain images either segment one slice of the
image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). …

Importance Preclinical studies have shown that transcranial near-infrared low-level light therapy
(LLLT) administered after traumatic brain injury (TBI) confers a neuroprotective response…

Simple Summary While deep learning has become a powerful tool in analysis of cancer
imaging, deep learning models have potential vulnerabilities that pose security threats in the …

… Arman Avesta Arman Avesta is on the trainee editorial board of Radiology: Artificial
Intelligence. Journal policy recused the author from having any role in the peer review of this …

Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming
process, especially in cases involving multiple metastases when the assessment is …

Simple Summary Peritumoral edema can contribute significantly to the development of
neurological symptoms in patients with brain metastases (METS), but the quantification of edema …

Objectives: Despite growing enthusiasm surrounding the utility of clinical informatics to improve
cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty …

Deep-learning methods for auto-segmenting brain images either segment one slice of the
image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). …

Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features
of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging …

BACKGROUND: The molecular profile of gliomas is a prognostic indicator for survival,
driving clinical decision-making for treatment. Pathology-based molecular diagnosis is …