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Liver Neoplasms

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An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B.

Journal of hepatology
BACKGROUND & AIMS: Several models have recently been developed to predict risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Our aims were to develop and validate an artificial intelligence-assisted prediction model of...

Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study.

Clinical & experimental metastasis
Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tom...

An attention-based deep learning model for predicting microvascular invasion of hepatocellular carcinoma using an intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging.

Physics in medicine and biology
The intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging (IVIM-DWI) with a series of images with different-values has great potential as a tool for detecting, diagnosing, staging, and monitoring disease progression or ...

Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions.

IEEE journal of biomedical and health informatics
The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, but existing analyses rarely adopt quantitative methods because the manual quantification of histopathological features is too expensive. We aim to ac...

Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data.

Scientific reports
Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmen...

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning.

BMC cancer
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, ...

GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.

International journal of molecular sciences
Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circR...

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer.

Journal of integrative medicine
OBJECTIVE: By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical r...