A5025978352- Liver Disease Diagnosis and Treatment
- Hepatocellular Carcinoma Treatment and Prognosis
- Liver Disease and Transplantation
- Radiomics and Machine Learning in Medical Imaging
- Metabolomics and Mass Spectrometry Studies
- MRI in cancer diagnosis
- Renal cell carcinoma treatment
- Cardiovascular Disease and Adiposity
- Advanced MRI Techniques and Applications
- AI in cancer detection
- Field-Flow Fractionation Techniques
Semmelweis University
2022-2025
Background: we evaluated regression models based on quantitative ultrasound (QUS) parameters and compared them with a vendor-provided method for calculating the fat fraction (USFF) in metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: We measured attenuation coefficient (AC) backscatter-distribution (BSC-D) determined USFF during calculated magnetic resonance imaging proton-density (MRI-PDFF) steatosis grade (S0-S4) combined retrospective-prospective cohort. trained...
Background and Objectives: This study aims to evaluate artificial intelligence-calculated hepatorenal index (AI-HRI) as a diagnostic method for hepatic steatosis. Materials Methods: We prospectively enrolled 102 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD). All had quantitative ultrasound (QUS), including AI-HRI, attenuation coefficient (AC,) backscatter-distribution (SC) measurements. The ultrasonographic indicator (US-FLI) score was also calculated. magnetic...
(1) Background: Open-source software tools are available to estimate proton density fat fraction (PDFF). (2) Methods: We compared four algorithms: complex-based with graph cut (GC), magnitude-based (MAG), magnitude-only estimation Rician noise modeling (MAG-R), and multi-scale quadratic pseudo-Boolean optimization (QPBO). The accuracy reliability of the methods were evaluated in phantoms known fat/water ratios a patient cohort various grades (S0-S3) steatosis. Image acquisitions performed at...
Liver tumors constitute a major part of the global disease burden, often making regular imaging follow-up necessary. Recently, deep learning (DL) has increasingly been applied in this research area. How these methods could facilitate report writing is still question, which our study aims to address by assessing multiple DL using Medical Open Network for Artificial Intelligence (MONAI) framework, may provide clinicians with preliminary information about given liver lesion. For purpose, we...
This study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear renal carcinomas (ccRCC) that is robust against institutional imaging protocols scanners.Preoperative unenhanced (UN), corticomedullary (CM), excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) extracted tumor volumes in each contrast phase. For ML...
We aimed to develop a non-linear regression model that could predict the fat fraction of liver (USFF), similar magnetic resonance imaging proton density (MRI-PDFF), based on quantitative ultrasound (QUS) parameters. measured and retrospectively collected attenuation coefficient (AC), backscatter-distribution (BSC-D), stiffness (LS) using shear wave elastography (SWE) in 90 patients with clinically suspected non-alcoholic fatty disease (NAFLD) 51 metabolic-associated (MAFLD). The MRI-PDFF was...
We aimed to develop a non-linear regression model that could predict the fat fraction of liver (UEFF), similar magnetic resonance imaging proton density (MRI-PDFF), based on quantitative ultrasound (QUS) parameters. measured and retrospectively collected attenuation coefficient (AC), backscatter-distribution (BSC-D), stiffness (LS) using shear wave elastography (SWE) in 90 patients with clinically suspected non-alcoholic fatty disease (NAFLD), 51 metabolic-associated (MAFLD). The MRI-PDFF...