Objective: To investigate the magnetic resonance imaging (MRI) features of well-differentiated hepatocellular carcinoma (HCC).
Methods: We reviewed the MRI of 32 patients with 33 pathologically confirmed well-differentiated HCC. The MRI protocol included T2-weighted imaging with and without fat saturation, dual-phase T1-weighted imaging, and gadolinium-enhanced dynamic study. The signal intensity of each lesion was categorized as hyperintense, isointense, and hypointense with reference to the surrounding liver parenchyma.
Results: Thirty-one (93.9%) of 33 well-differentiated HCC were demonstrated on the MRI. The remaining 2 were isointense in all magnetic resonance sequences and, therefore, could not be identified. Most of them were hyperintense (n = 15 [45.4%]) or isointense (n = 16 [48.5%]) on T1-weighted imaging, and hyperintense (n = 12 [36.4%]) or isointense (n = 17 [51.5%]) on T2-weighted imaging. On the dynamic study, 17 lesions (51.5%) were enhanced.
Conclusions: MRI may identify most well-differentiated HCC; however, the imaging appearance is diverse. Biopsy should be performed if magnetic resonance study is inconclusive.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/00004728-200607000-00008 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Optimized Synthetic Correlated Diffusion Imaging for Improving Breast Cancer Tumor Delineation.
Sensors (Basel)
December 2024
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Breast cancer is a significant cause of death from cancer in women globally, highlighting the need for improved diagnostic imaging to enhance patient outcomes. Accurate tumor identification is essential for diagnosis, treatment, and monitoring, emphasizing the importance of advanced imaging technologies that provide detailed views of tumor characteristics and disease. Recently, a new imaging modality named synthetic correlated diffusion imaging (CDI) has been showing promise for enhanced prostate cancer delineation when compared to existing MRI imaging modalities.
View Article and Find Full Text PDFMachine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.
Sensors (Basel)
December 2024
Faculty of Computer Science, Polish-Japanese Academy of Information Technology, 86 Koszykowa Street, 02-008 Warsaw, Poland.
Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans ( = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated.
View Article and Find Full Text PDFMR Elastography Using the Gravitational Transducer.
Sensors (Basel)
December 2024
Research Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London WC2R 2LS, UK.
MR elastography is a non-invasive imaging technique that provides quantitative maps of tissue biomechanical properties, i.e., elasticity and viscosity.
View Article and Find Full Text PDFStrategies to Stabilize Dalbavancin in Aqueous Solutions; Section-2: The Effects of 2 Hydroxypropyl-β-Cyclodextrin and Acetate Buffer with and Without Divalent Metal Ions.
Pharmaceutics
November 2024
Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.
The effect of 2-hydroxpropyl-β-cyclodextrin (2HPβCD) with or without divalent metal ions (Ca, Mg, and Zn) on the stability of dalbavancin in acetate buffer was investigated. Dalbavancin recovery from formulations with 2HPβCD and divalent metal ions after four weeks of storage at 5 °C and 55 °C was measured by RP-HPLC and HP-SEC; a longer-term study was carried out over six months at 5 °C, 25 °C, and 40 °C. Binding of 2HPβCD was characterized by isothermal titration calorimetry (ITC) and nuclear magnetic resonance (NMR).
View Article and Find Full Text PDFExploring 3D Printing in Drug Development: Assessing the Potential of Advanced Melt Drop Deposition Technology for Solubility Enhancement by Creation of Amorphous Solid Dispersions.
Pharmaceutics
November 2024
Merck Life Science KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany.
Melt-based 3D printing technologies are currently extensively evaluated for research purposes as well as for industrial applications. Classical approaches often require intermediates, which can pose a risk to stability and add additional complexity to the process. The Advanced Melt Drop Deposition (AMDD) technology, is a 3D printing process that combines the principles of melt extrusion with pressure-driven ejection, similar to injection molding.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!