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Deep learning with convolutional neural network in radiology
Deep learning with a convolutional neural network (CNN) is gaining attention recently for its
high performance in image recognition. Images themselves can be utilized in a learning …
high performance in image recognition. Images themselves can be utilized in a learning …
Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma
E Harding‐Theobald, J Louissaint… - Alimentary …, 2021 - Wiley Online Library
Background Advances in imaging technology have the potential to transform the early
diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image …
diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image …
Radiomics in liver diseases: Current progress and future opportunities
Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have
become an increasingly significant health problem worldwide. Noninvasive imaging plays a …
become an increasingly significant health problem worldwide. Noninvasive imaging plays a …
Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and …
Objective To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from
cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on …
cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on …
The role of radiomics and AI technologies in the segmentation, detection, and management of hepatocellular carcinoma
Simple Summary As a primary hepatic tumor, hepatocellular carcinoma (HCC) is the most
prevalent kind. Recent developments in magnetic resonance imaging (MRI) and computed …
prevalent kind. Recent developments in magnetic resonance imaging (MRI) and computed …
[HTML][HTML] Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest
Rationale and objectives To investigate the impact of random survival forest (RSF) classifier
trained by radiomics features over the prediction of the overall survival of patients with …
trained by radiomics features over the prediction of the overall survival of patients with …
[HTML][HTML] Diffusion-weighted MRI and diffusion kurtosis imaging to detect RAS mutation in colorectal liver metastasis
Simple Summary Imaging derived parameters can provide data on tumor phenotype as well
as cancer microenvironment. Radiomics has recently shown potential in realizing …
as cancer microenvironment. Radiomics has recently shown potential in realizing …
Intratumoral heterogeneity of hepatocellular carcinoma: From single-cell to population-based studies
Q Zhang, Y Lou, XL Bai… - World journal of …, 2020 - pmc.ncbi.nlm.nih.gov
Hepatocellular carcinoma (HCC) is characterized by high heterogeneity in both intratumoral
and interpatient manners. While interpatient heterogeneity is related to personalized …
and interpatient manners. While interpatient heterogeneity is related to personalized …
A deep survival interpretable radiomics model of hepatocellular carcinoma patients
This work aims to identify a new radiomics signature using imaging phenotypes and clinical
variables for risk prediction of overall survival (OS) in hepatocellular carcinoma (HCC) …
variables for risk prediction of overall survival (OS) in hepatocellular carcinoma (HCC) …
Radiogenomics and radiomics in liver cancers
Radiogenomics is a computational discipline that identifies correlations between cross-
sectional imaging features and tissue-based molecular data. These imaging phenotypic …
sectional imaging features and tissue-based molecular data. These imaging phenotypic …