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MRI Radiomics Analysis in the Diagnostic Differentiation of Malignant Soft Tissue Myxoid Sarcomas From Benign Soft Tissue Musculoskeletal Myxomas.
J Magn Reson Imaging
January 2025
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
Background: Differentiation of benign myxomas and malignant myxoid sarcomas can be difficult with an overlapping spectrum of morphologic MR findings.
Purpose: To assess the diagnostic utility of MRI radiomics in the differentiation of musculoskeletal myxomas and myxoid sarcomas.
Study Type: Retrospective.
Clinical Evaluation of 3D Motion-Correction Via Scout Accelerated Motion Estimation and Reduction Framework Versus Conventional T1-Weighted MRI at 1.5 T in Brain Imaging.
Invest Radiol
January 2025
From the Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Mainz, Germany (L.S.L., K.H.H., A.K., M.A.B., S.A., A.E.O.); Institute of Medical Biostatistics, Epidemiology, and Informatics, University Medical Center Mainz, Johannes Gutenberg University, Mainz, Germany (R.H.P.); and Siemens Healthineers AG, Forchheim, Germany (D.P., D.N.S.).
Objectives: The aim of this study was to investigate the occurrence of motion artifacts and image quality of brain magnetic resonance imaging (MRI) T1-weighted imaging applying 3D motion correction via the Scout Accelerated Motion Estimation and Reduction (SAMER) framework compared with conventional T1-weighted imaging at 1.5 T.
Materials And Methods: A preliminary study involving 14 healthy volunteers assessed the impact of the SAMER framework on induced motion during 3 T MRI scans.
Role of Artificial Intelligence in MRI-Based Rectal Cancer Staging: A Systematic Review.
Cureus
December 2024
Internal Medicine, Belgaum Institute of Medical Science, Belgaum, IND.
Several studies explored the application of artificial intelligence (AI) in magnetic resonance imaging (MRI)-based rectal cancer (RC) staging, but a comprehensive evaluation remains lacking. This systematic review aims to review the performance of AI models in MRI-based RC staging. PubMed and Embase were searched from the inception of the database till October 2024 without any language and year restrictions.
View Article and Find Full Text PDFBrain tumor enhancement prediction from pre-contrast conventional weighted images using synthetic multiparametric mapping and generative artificial intelligence.
Quant Imaging Med Surg
January 2025
Radiology and Nuclear Medicine Department, Erasmus MC, Rotterdam, The Netherlands.
Background: Gadolinium-based contrast agents (GBCAs) are usually employed for glioma diagnosis. However, GBCAs raise safety concerns, lead to patient discomfort and increase costs. Parametric maps offer a potential solution by enabling quantification of subtle tissue changes without GBCAs, but they are not commonly used in clinical practice due to the need for specifically targeted sequences.
View Article and Find Full Text PDFRadiomic signatures of brain metastases on MRI: utility in predicting pathological subtypes of lung cancer.
Transl Cancer Res
December 2024
Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Background: The pathological sub-classification of lung cancer is crucial in diagnosis, treatment and prognosis for patients. Quick and timely identification of pathological subtypes from imaging examinations rather than histological tests could help guiding therapeutic strategies. The aim of the study is to construct a non-invasive radiomics-based model for predicting the subtypes of lung cancer on brain metastases (BMs) from multiple magnetic resonance imaging (MRI) sequences.
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