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Carcinoma, Hepatocellular

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Machine learning and experimental screening of chromatin regulator signatures and potential drugs in hepatitis B related hepatocellular carcinoma.

Journal of biomolecular structure & dynamics
Many evidences have confirmed that chromatin regulator factors (CRs) are involved in the progression of cancer, but its potential mechanism of affecting hepatitis B related hepatocellular carcinoma still needs to be studied. Our study detected the CR...

Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma.

Abdominal radiology (New York)
PURPOSE: To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magne...

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Nature communications
Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed ...

CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning.

Academic radiology
RATIONALE AND OBJECTIVES: To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting micr...

Deep learning approach for discrimination of liver lesions using nine time-phase images of contrast-enhanced ultrasound.

Journal of medical ultrasonics (2001)
PURPOSE: Contrast-enhanced ultrasound (CEUS) shows different enhancement patterns depending on the time after administration of the contrast agent. The aim of this study was to evaluate the diagnostic performance of liver nodule characterization usin...

Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

Abdominal radiology (New York)
OBJECTIVES: The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (cHCC-ICC...

Deep learning based automatic internal gross target volume delineation from 4D-CT of hepatocellular carcinoma patients.

Journal of applied clinical medical physics
BACKGROUND: The location and morphology of the liver are significantly affected by respiratory motion. Therefore, delineating the gross target volume (GTV) based on 4D medical images is more accurate than regular 3D-CT with contrast. However, the 4D ...

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the predic...

Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

Academic radiology
RATIONALE AND OBJECTIVES: Recurrence of hepatocellular carcinoma (HCC) is a major concern in its management. Accurately predicting the risk of recurrence is crucial for determining appropriate treatment strategies and improving patient outcomes. A ce...