Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid–enhanced MRI

Hectors, Stefanie; Kennedy, Paul; Huang, Kuang-Han; Stocker, Daniel; Carbonell, Guillermo; Greenspan, Hayit; Friedman, Scott L.; Taouli, Bachir

doi:10.1007/s00330-020-07475-4

Cited by 52 publications

(25 citation statements)

References 25 publications

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“…Using histopathology as a gold standard, the percentage of correct assessment of the ML approach was 85.7% with an area under the curve (AUC) of 0.82, compared to 0.92 for magnetic resonance elastography (MRE) [30]. Another study showed similar results, but using a gadoxetic acidenhanced MRI, with an increasing AUC in more severe liver fibrosis [31]. Similar to fibrosis, AI algorithms were applied to quantify liver steatosis.…”

Section: Quantificationmentioning

confidence: 92%

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Cardobi

Palù

Pedrini

et al. 2021

Cancers

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Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.

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Section: Quantificationmentioning

confidence: 92%

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Cardobi

Palù

Pedrini

et al. 2021

Cancers

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“…They achieved AUROCs of 0.84, 0.84 and 0.85 for classifying into cirrhosis, advanced fibrosis and substantial fibrosis respectively. They were unable to differentiate fibrosis scores as well as Hectors et al [ 60 ] with similar methods, likely due to the Hectors study pre-training on Image Net data and so compensating for the small datasets that these study have to train on. Radiomics combined with a logistic regression model has also been used to classify into liver fibrosis scores.…”

Section: Classificationmentioning

confidence: 95%

“…Liver fibrosis staging is used clinically in predicting the prognosis of liver diseases and helps in determining the appropriate action to take in treatment[ 60 ]. Several approaches of AI applications on liver MR have been described for the assessment of liver fibrosis.…”

Section: Classificationmentioning

confidence: 99%

“…Several approaches of AI applications on liver MR have been described for the assessment of liver fibrosis. Hectors et al [ 60 ], used a VGG16 network to predict the fibrosis stage from F1-4 using Gd-EOB-DTPA-enhanced liver MR images. The network, which was pretrained on image net with only the last few layers being trainable, predicted a class from F1-F4, F2-F4, F3-F4 and F4, achieving an AUROC of 0.77, 0.91, 0.91 and 0.85 respectively, showing good diagnostic ability.…”

Section: Classificationmentioning

confidence: 99%

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Emerging artificial intelligence applications in liver magnetic resonance imaging

Hill¹,

Biasiolli²,

Robson³

et al. 2021

WJG

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Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.

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“…Compared with CT, there are few DL studies on magnetic resonance imaging (MRI). Most current research has focused on the diagnosis of liver, pancreatic, and rectal diseases, such as liver cancer [ 45 ], liver fibrosis [ 46 ], liver fat segmentation [ 47 ], pancreatic tumors [ 48 ], rectal cancer [ 49 ], etc. Abdominal organ segmentation and fat segmentation are the advantages of MRI.…”

Section: Application Of DL In Digestive System Imagingmentioning

confidence: 99%

A systematic review on application of deep learning in digestive system image processing

2021

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Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid–enhanced MRI

Cited by 52 publications

References 25 publications

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Emerging artificial intelligence applications in liver magnetic resonance imaging

A systematic review on application of deep learning in digestive system image processing

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