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Carcinoma, Hepatocellular

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Characterization of signature trends across the spectrum of non-alcoholic fatty liver disease using deep learning method.

Life sciences
AIMS: The timely diagnosis of different stages in NAFLD is crucial for disease treatment and reversal. We used hepatocellular ballooning to determine different NAFLD stages.

Contrast phase recognition in liver computer tomography using deep learning.

Scientific reports
Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis o...

Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM.

IEEE transactions on medical imaging
OBJECTIVE: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a ...

Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning.

Genes
Cancer prognosis analysis is of essential interest in clinical practice. In order to explore the prognostic power of computational histopathology and genomics, this paper constructs a multi-modality prognostic model for survival prediction. We collec...

A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Accurate preoperative identification of the microvascular invasion (MVI) can relieve the pressure from personalized treatment adaptation and improve the poor prognosis for hepatocellular carcinoma (HCC). This study aimed to develop and va...

A deep learning model with incorporation of microvascular invasion area as a factor in predicting prognosis of hepatocellular carcinoma after R0 hepatectomy.

Hepatology international
INTRODUCTION: Microvascular invasion (MVI) is a known risk factor for prognosis after R0 liver resection for hepatocellular carcinoma (HCC). The aim of this study was to develop a deep learning prognostic prediction model by incorporating a new facto...

Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review.

The British journal of radiology
OBJECTIVE: Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (...

LRFNet: A deep learning model for the assessment of liver reserve function based on Child-Pugh score and CT image.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Liver reserve function should be accurately evaluated in patients with hepatic cellular cancer before surgery to evaluate the degree of liver tolerance to surgical methods. Meanwhile, liver reserve function is also an import...

Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.

World journal of surgical oncology
BACKGROUND: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhan...

Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence.

Diagnostic and interventional imaging
PURPOSE: Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and t...