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Purpose: To study the application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer.

Methods: Different electronic databases were considered. Articles published in the last five years were analyzed (January 2019 and December 2023).

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Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability.

Neuroinformatics

January 2025

Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.

This paper introduces the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source, Python-based pipeline that consumes MR images of the brain and produces anatomical segmentations, lesion segmentations, and human-interpretable imaging features describing the lesions in the brain. ALFE pipeline is modeled after the neuroradiology workflow and generates features that can be used by physicians for quantitative analysis of clinical brain MRIs and for machine learning applications. The pipeline uses a decoupled design which allows the user to customize the image processing, image registrations, and AI segmentation tools without the need to change the business logic of the pipeline.

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Introduction: The venous outflow profile (VOP) is a crucial yet often overlooked aspect affecting stroke outcomes. It plays a major role in the physiopathology of acute cerebral ischemia, as it accounts for both the upstream arterial collaterals and cerebral microperfusion. This enables it to circumvent the limitations of various arterial collateral evaluation systems, which often fail to consider impaired autoregulation and its impact on cerebral blood flow at the microcirculatory levels.

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Background: Vascular risk factors captured in midlife represent modifiable features of cardiovascular disease (CVD), stroke, dementia, and dementia-related neuropathology. Subclinical measures of CVD may help identify specific structural and function aspects underlying vascular contributions to cognitive impairment and dementia over and above conventional dementia risk scores.

Method: The MESA study followed a diverse cohort of 6,814 adults aged 45-84 years over 6 clinical examinations and annual follow-up calls since baseline, 2000-2002.

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Biomarkers.

Alzheimers Dement

December 2024

Frontotemporal Disorders Unit and Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Background: Neurodegeneration in sporadic early-onset Alzheimer disease (EOAD) is topographically heterogeneous, as suggested by variability in syndromic presentation. We performed an unsupervised clustering analysis of structural MRI data to identify anatomical subtypes of EOAD. We hypothesized that distinct clusters will be present but will: (1) share areas of overlap focused around posterior regions of our newly developed EOAD signature of cortical atrophy (Touroutoglou et al.

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