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Liver Neoplasms

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Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images.

Sensors (Basel, Switzerland)
The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and com...

A closer look to the new frontier of artificial intelligence in the percutaneous treatment of primary lesions of the liver.

Medical oncology (Northwood, London, England)
The purpose of thermal ablation is induction of tumor death by means of localized hyperthermia resulting in irreversible cellular damage. Ablative therapies are well-recognized treatment modalities for HCC lesions and are considered standard of care ...

Deep learning-based liver segmentation for fusion-guided intervention.

International journal of computer assisted radiology and surgery
PURPOSE: Tumors often have different imaging properties, and there is no single imaging modality that can visualize all tumors. In CT-guided needle placement procedures, image fusion (e.g. with MRI, PET, or contrast CT) is often used as image guidanc...

Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network.

Scientific reports
Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Auto-segmentation of abdominal organs has been made possible by the advent of the convolutional neural network. We utilized the U-Net, a 3D-patch-based convolutio...

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.

Genomics
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understan...

Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases.

Scientific reports
Hepatocellular carcinoma (HCC) is a common malignant tumor in China. In the present study, we aimed to construct and verify a prediction model of recurrence in HCC patients using databases (TCGA, AMC and Inserm) and machine learning methods and obtai...

Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.

European journal of radiology
PURPOSE: To develop and evaluate the performance of a fully-automated convolutional neural network (CNN)-based algorithm to evaluate hepatobiliary phase (HBP) adequacy of gadoxetate disodium (EOB)-enhanced MRI. Secondarily, we explored the potential ...

Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning.

Annals of surgical oncology
OBJECTIVE: The aim of this study was to develop quantitative feature-based models from histopathological images to distinguish hepatocellular carcinoma (HCC) from adjacent normal tissue and predict the prognosis of HCC patients after surgical resecti...

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

European radiology
OBJECTIVES: We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) session by quantitatively analy...

Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma.

Nature reviews. Gastroenterology & hepatology
Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agentsĀ have demonstrated efficacy in clinical trials, including the targeted thera...