AIMC Topic: Liver Neoplasms

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Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning.

Abdominal radiology (New York)
PURPOSE: Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating th...

Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy.

Medical physics
PURPOSE: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during ...

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...