Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines.
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http://dx.doi.org/10.1038/srep21417 | DOI Listing |
J Headache Pain
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Background: Huai-he Hospital of Henan University, Kaifeng, China. Electronic address:
Objective: To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors.
Methods: Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO).
J Imaging Inform Med
January 2025
Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.
Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades.
View Article and Find Full Text PDFAcad Radiol
December 2024
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104 (D.A.T.). Electronic address:
Eur Radiol
December 2024
Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Objectives: Distinguishing dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) dementia, particularly in patients with DLB and concomitant AD pathology (DLB/AD+), can be challenging and there is no specific MRI signature for DLB. The aim of this study is to examine the additional value of MRI-based brain volumetry in separating patients with DLB (AD+/-) from patients with AD and controls.
Methods: We included 1518 participants from four cohorts (ADC, ADNI, PDBP and PredictND); 147 were patients with DLB (n = 76, DLB/AD+; n = 71, DLB/AD-), 668 patients with AD dementia, and 703 controls.
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