hepatocellular carcinoma

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

Precision of MRI radiomics features in the liver and HCC

The aim of this study, consisting of a population of 55 patients who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month, was to assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma. The authors determined that MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI

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Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

Of late, deep learning-based algorithms have been successfully applied to various medical imaging modalities, ranging from chest radiographs to head CT scans. Compared to other body parts, there is a paucity of data regarding the application of deep learning-based algorithms in the liver. This can be attributed to the following reasons: First, unlike other body parts usually relying on single-phase

Read More →

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics

The purpose of this single-center retrospective study was to investigate the effectiveness of contrast-enhanced computed tomography (CECT)-based radiomic signatures for the preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning. The authors found that the radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC. Key points The radiomics signatures may

Read More →

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Advanced hepatocellular carcinoma (HCC) carries a dismal prognosis. For a decade, sorafenib, a multi-kinase inhibitor, was the only approved systemic therapy for HCC. However, its response rate in advanced HCC is only about 2%. The last few years have seen rapid approval of additional systemic therapies for HCC, including immunotherapy strategies. Immune checkpoint inhibitor nivolumab has a promising reported response

Read More →

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

The aim of this study was to establish and validate an artificial intelligence-based radiomics strategy in order to predict personalized responses to hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) by analyzing contrast-enhanced ultrasound (CEUS) cines quantitatively. This was done using 130 HCC patients, showing that a deep learning-based radiomics method can effectively utilize CEUS, resulting in accurate and personalized

Read More →

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules

This article sets out to determine whether machine learning can be used to train and calibrate the signature for diagnosing hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. The authors proved that artificial intelligence could enhance clinicians’ decision and reduce the rate of cirrhotic patients requiring liver biopsy. Key points In cirrhotic patients with visually indeterminate liver nodules, expert

Read More →

Precision of MRI radiomics features in the liver and HCC

The aim of this study, consisting of a population of 55 patients who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month, was to assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma. The authors determined that MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI

Read More →

Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

Of late, deep learning-based algorithms have been successfully applied to various medical imaging modalities, ranging from chest radiographs to head CT scans. Compared to other body parts, there is a paucity of data regarding the application of deep learning-based algorithms in the liver. This can be attributed to the following reasons: First, unlike other body parts usually relying on single-phase

Read More →

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics

The purpose of this single-center retrospective study was to investigate the effectiveness of contrast-enhanced computed tomography (CECT)-based radiomic signatures for the preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning. The authors found that the radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC. Key points The radiomics signatures may

Read More →

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Advanced hepatocellular carcinoma (HCC) carries a dismal prognosis. For a decade, sorafenib, a multi-kinase inhibitor, was the only approved systemic therapy for HCC. However, its response rate in advanced HCC is only about 2%. The last few years have seen rapid approval of additional systemic therapies for HCC, including immunotherapy strategies. Immune checkpoint inhibitor nivolumab has a promising reported response

Read More →

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

The aim of this study was to establish and validate an artificial intelligence-based radiomics strategy in order to predict personalized responses to hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) by analyzing contrast-enhanced ultrasound (CEUS) cines quantitatively. This was done using 130 HCC patients, showing that a deep learning-based radiomics method can effectively utilize CEUS, resulting in accurate and personalized

Read More →

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules

This article sets out to determine whether machine learning can be used to train and calibrate the signature for diagnosing hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. The authors proved that artificial intelligence could enhance clinicians’ decision and reduce the rate of cirrhotic patients requiring liver biopsy. Key points In cirrhotic patients with visually indeterminate liver nodules, expert

Read More →

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