Purpose: To assess Blood Oxygen Level-Dependent (BOLD) Magnetic Resonance Imaging (MRI) for noninvasive preoperative prediction of Microvascular Invasion (MVI) in Hepatocellular Carcinoma (HCC).
Materials And Methods: In this prospective, institutional review board approved study, 26 patients (21 men and 5 women age range, 34-77 years with mean age of 61 years) with HCC were evaluated preoperatively with liver MRI including baseline and post oxygen (O2) breathing BOLD MRI. Post processing of MRI data was performed to obtain R2* values (1/s) and correlated with histopathological assessment of MVI. Statistical analysis was performed to assess correlation of baseline R2*, post O2 R2* and R2* ratios to presence of MVI in HCC by binary logistic regression analysis.
Results: MVI was present in 15/26 (58%) of HCC on histopathology. The mean R2* values ± SD at baseline and post O2 with and without MVI were 35 ± 12, 36 ± 12, 38 ± 10, 42 ± 17. The R2* values between the groups with and without MVI were not significantly different statistically.
Conclusion: BOLD MRI is unable to accurately predict MVI in HCC. The noninvasive preoperative MRI detection of MVI in HCC remains elusive.
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http://dx.doi.org/10.1002/jmri.23858 | DOI Listing |
J Fish Biol
January 2025
Polar branch of the Russian Federal Research Institute of Fisheries and Oceanography ("PINRO" named after N.M. Knipovich), Murmansk, Russia.
More than 27,000 stomachs from 70 species of fish were collected from the Barents Sea in 2015. Quantitative stomach content expressed relative to the body weight of the predator fish (g g as %) varied by four to five orders of magnitude for six species with the largest sample size (Atlantic cod Gadus morhua, haddock Melanogrammus aeglefinus, Greenland halibut Reinhardtius hippoglossoides, long rough dab Hippoglossoides platessoides, polar cod Boreogadus saida, and Atlantic capelin Mallotus villosus). The quantitative stomach contents of individual fish followed a common and strict statistical relationship for predator species or groups of species (by families), and for prey categories across predator species.
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January 2025
Department of Oral & Maxillofacial Surgery and Diagnostic Sciences, Faculty of Dentistry, Taif University, 21944, Taif, Saudi Arabia.
This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance.
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January 2025
Department of Neurosurgery, University Hospital Tübingen, Tübingen, Germany.
To compare 1D (linear) tumor volume calculations and classification systems with 3D-segmented volumetric analysis (SVA), focusing specifically on their effectiveness in the evaluation and management of NF2-associated vestibular schwannomas (VS). VS were clinically followed every 6 months with cranial, thin-sliced (< 3 mm) MRI. We retrospectively reviewed and used T1-weighted post-contrast enhanced (gadolinium) images for both SVA and linear measurements.
View Article and Find Full Text PDFUltrasound Med Biol
January 2025
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong; Biomedical Engineering Programme, The University of Hong Kong, Hong Kong. Electronic address:
Objective: Near-field (NF) clutter filters are critical for unveiling true myocardial structure and dynamics. Randomized singular value decomposition (rSVD) stands out for its proven computational efficiency and robustness. This study investigates the effect of rSVD-based NF clutter filtering on myocardial motion estimation.
View Article and Find Full Text PDFEnviron Pollut
January 2025
School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. Electronic address:
The soils/sediments organic carbon sorption coefficient (K) of organic substances is one of the indispensable environmental behavioral parameters in chemicals management. Because the test procedure used to measure K is normally expensive and time-consuming, predictive methods are considered vitally important technology to fill the data gap of K. In this study, quantitative structure-property relationship (QSPR) models are developed using a data set with 1477 experimental logK values and seven typical machine learning algorithms.
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