AI Medical Compendium Topic:
Liver Neoplasms

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MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

Physics in medicine and biology Jul 16, 2019
Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-onl...
Humans Retrospective Studies Tomography, X-Ray Computed Image Processing, Computer-Assisted Magnetic Resonance Imaging Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiography, Abdominal Reproducibility of Results Deep Learning Liver Neoplasms Carcinoma, Hepatocellular
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Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning.

Proceedings of the National Academy of Sciences of the United States of America Jul 8, 2019
Metastasis of solid tumors is a key determinant of cancer patient survival. Targeting micrometastases using nanoparticles could offer a way to stop metastatic tumor growth before it causes excessive patient morbidity. However, nanoparticle delivery t...
Neoplasm Micrometastasis Mice Nanoparticles Mice, Inbred BALB C Image Interpretation, Computer-Assisted Machine Learning Female Animals Liver Neoplasms Imaging, Three-Dimensional Lung Neoplasms
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Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks.

Academic radiology May 28, 2019
OBJECTIVES: To develop a deep learning-based algorithm to automatically identify optimal portal venous phase timing (PVP-timing) so that image analysis techniques can be accurately performed on post contrast studies.
Humans Liver Neoplasms Contrast Media Portal Vein Clinical Trials as Topic Neural Networks, Computer Algorithms Retrospective Studies Deep Learning
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Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections.

PloS one May 20, 2019
PURPOSE: To compare manual corrections of liver masks produced by a fully automatic segmentation method based on convolutional neural networks (CNN) with manual routine segmentations in MR images in terms of inter-observer variability and interaction...
Observer Variation Carcinoma, Hepatocellular Magnetic Resonance Imaging Humans Liver Image Interpretation, Computer-Assisted Neural Networks, Computer Algorithms Liver Neoplasms
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Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

European radiology May 15, 2019
OBJECTIVES: To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.
Carcinoma, Hepatocellular Magnetic Resonance Imaging Predictive Value of Tests Image Interpretation, Computer-Assisted Male Middle Aged Deep Learning Liver Neoplasms Bile Duct Neoplasms Proof of Concept Study Cholangiocarcinoma Bile Ducts, Intrahepatic Retrospective Studies Female Aged Adult Humans Neural Networks, Computer Machine Learning Algorithms
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Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?

International journal of computer assisted radiology and surgery May 3, 2019
PURPOSE: To evaluate the effect of image registration on the diagnostic performance of transfer learning (TL) using pretrained convolutional neural networks (CNNs) and three-phasic dynamic contrast-enhanced computed tomography (DCE-CT) for primary li...
Humans Neural Networks, Computer Algorithms Retrospective Studies Contrast Media Tomography, Spiral Computed Middle Aged Female Liver Neoplasms Reproducibility of Results Carcinoma, Hepatocellular Aged Image Processing, Computer-Assisted Male
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Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.

International journal of computer assisted radiology and surgery Apr 30, 2019
PURPOSE: We address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on multiphase CT images using a deep learning approach.
Humans Neural Networks, Computer Algorithms Biomarkers Tomography, X-Ray Computed Reproducibility of Results Deep Learning Liver Neoplasms Carcinoma, Hepatocellular Image Processing, Computer-Assisted Liver Pattern Recognition, Automated Necrosis
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A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks.

Sensors (Basel, Switzerland) Apr 28, 2019
Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neur...
Glioma Support Vector Machine Pituitary Neoplasms Wavelet Analysis Meningioma Tomography, X-Ray Computed Sensitivity and Specificity Brain Neoplasms Liver Neoplasms Humans Neural Networks, Computer
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Machine Learning for Medical Imaging.

Journal of healthcare engineering Apr 28, 2019
Support Vector Machine Algorithms Principal Component Analysis Diagnostic Imaging Alzheimer Disease Humans Neural Networks, Computer Machine Learning Software Reproducibility of Results Deep Learning Liver Neoplasms
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Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.

European radiology Apr 23, 2019
OBJECTIVES: To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.
Humans Neural Networks, Computer Female Liver Neoplasms Carcinoma, Hepatocellular United States Magnetic Resonance Imaging Cholangiocarcinoma Bile Ducts, Intrahepatic Image Interpretation, Computer-Assisted Bile Duct Neoplasms Male Reproducibility of Results Middle Aged Aged Deep Learning Adult ROC Curve Sensitivity and Specificity
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