Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis

Li, Wei; Huang, Yang; Zhuang, Bowen; Liu, Guangjian; Hu, Hang-Tong; Li, Xin; Liang, Jin-Yu; Wang, Zhu; Huang, Xiaowen; Zhang, Chu-Qing; Ruan, Si-Min; Xie, Xiaoyan; Kuang, Ming; Lü, Ming-De; Chen, Li-Da; Wang, Wei

doi:10.1007/s00330-018-5680-z

Cited by 102 publications

(77 citation statements)

References 39 publications

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“…Feature selection and classification can be performed together as a single process or separately using different algorithms. Unreliable features may be excluded prior to feature selection and classification, based on the results of interor intra-observer agreement or test-retest repeatability analyses (6,11,(17)(18)(19)(20)(21). To reduce redundancy in the features, informative features showing a high dynamic range may be selected among the correlated features in hierarchical feature clustering (18,22).…”

Section: Process Of Radiomics Analysismentioning

confidence: 99%

“…Traditional statistical methods may not be successful in dealing with high-dimensional radiomics features (i.e., too many variables relative to the number of observations). A number of machine learning methods have therefore been used for feature selection and/or classification (10,21,23,24). Among the methods for feature selection and classification, regression with Ridge, least absolute shrinkage and selection operator (LASSO), and elastic net regularization have been commonly used (6, 10-13, 17, 19, 25, 26), likely because these algorithms allow for the development of a regression model that is more familiar to radiologists than other machine learning classifiers.…”

Section: Process Of Radiomics Analysismentioning

confidence: 99%

“…Several exploratory studies have indicated the potential of radiomics of multiparametric ultrasound (21) and histogram features of CT images (29,30) in staging liver fibrosis and in diagnosing nonalcoholic steatohepatitis. However, the results of these studies were not conclusive because of the small study populations and the lack of proper validation (21,29,30).…”

Section: Chronic Liver Diseasementioning

confidence: 99%

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Radiomics and Deep Learning: Hepatic Applications

2020

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Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

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Section: Process Of Radiomics Analysismentioning

confidence: 99%

Section: Process Of Radiomics Analysismentioning

confidence: 99%

Section: Chronic Liver Diseasementioning

confidence: 99%

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Radiomics and Deep Learning: Hepatic Applications

2020

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“…Eventually, a total of 78 articles were included in the qualitative analysis, of which 17 were included in the quantitative analysis (15 studies on liver brosis and 2 studies on NAFLD). There were 11 studies integrating AI with imaging modalities, i.e., ultrasonography (21)(22)(23)(24)(25) , elastography (26,27) , computed tomography (CT) (28,29) and magnetic resonance imaging (MRI) (30,31) , to facilitate the diagnosis of liver brosis and NAFLD. The other 6 studies developed AI models using clinical and laboratory data, such as the presence of other underlying diseases or ascites, liver chemistry tests, and platelet and white blood cell counts, to predict liver brosis stages (32)(33)(34)(35)(36)(37) .…”

Section: Literature Searchmentioning

confidence: 99%

Application of Artificial Intelligence in Chronic Liver Diseases: A Systematic Review and Meta-analysis

Decharatanachart

Chaiteerakij

Tiyarattanachai

et al. 2020

Preprint

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Background: The gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Various noninvasive modalities, e.g., ultrasonography, elastography and clinical predictive scores, have been used as alternatives to liver biopsy, with limited performance. Recently, artificial intelligence (AI) models have been developed and integrated into noninvasive diagnostic tools to improve their performance. Methods: We systematically searched for studies on AI-assisted diagnosis of liver fibrosis and NAFLD on MEDLINE, Scopus, Web of Science and Google Scholar. The pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic odds ratio (DOR) with their 95% confidence intervals (95%CIs) were calculated using a random effects model. A summary receiver operating characteristic curve and the area under the curve was generated to determine the diagnostic accuracy of the AI-assisted system. Subgroup analyses by diagnostic modalities were performed.Results: We included 17 studies reporting the performances of AI-assisted ultrasonography, elastrography, computed tomography (CT), magnetic resonance imaging (MRI) and clinical parameters for the diagnosis of liver fibrosis and steatosis. For the diagnosis of liver fibrosis, the pooled sensitivity, specificity, PPV, NPV and DOR were 0.78 (0.70 – 0.84), 0.88 (0.79 – 0.94), 0.73 (0.58 – 0.84), 0.91 (0.88 – 0.94) and 29.57 (10.65 – 82.10), respectively, for cirrhosis; 0.86 (0.80 – 0.91), 0.87 (0.79 – 0.92), 0.85 (0.74 – 0.91), 0.88 (0.82 – 0.92) and 37.95 (15.52 – 92.77), respectively; for advanced fibrosis; and 0.86 (0.78 – 0.92), 0.81 (0.77 – 0.84), 0.88 (0.80 – 0.93), 0.77 (0.58 – 0.89) and 26.79 (14.47 – 49.62), respectively, for significant fibrosis. Subgroup analyses showed significant differences in performance for the diagnosis of fibrosis among different modalities. The pooled sensitivity, specificity, PPV, NPV and DOR were 0.97 (0.76 – 1.00), 0.91 (0.78 – 0.97), 0.95 (0.87 – 0.98), 0.93 (0.80 – 0.98) and 191.52 (38.82 – 944.81), respectively, for the diagnosis of liver steatosis. Conclusions: AI-assisted systems have promising potential for the diagnosis of liver fibrosis and NAFLD. Validations of their performances are warranted before implementing these AI-assisted systems in clinical practice.Trial registration: The protocol was registered with PROSPERO (CRD42020183295).

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“…For example, a study that followed 11,448 subjects for 5 years showed that the incidence of a disease was 12% (n = 1,418) [1]. Another cohort study followed 77,425 free of liver disease subjects for 4.5 years, 10,340 of them have developed the disease [2]. In general, more than 75% of liver tissue requires to be affected before the function of the liver is decreased [3].…”

Section: Introductionmentioning

confidence: 99%

A Predictive model for liver disease progression based on logistic regression algorithm

Abdalrada

Yahya

Alaidi

et al. 2019

PEN

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Liver disease counts to be one of the most prevalent diseases in the worldwide. Therefore, this paper is aim to address the problem of predicting liver disease progression. As the existing predictive models focus on predicting the label of disease; the probability of developing the disease is still obscure. This paper, therefore, has proposed a model to predict the probability occurrence of liver diseases. The proposed predictive model used logistic regression abilities to predict the probability of liver disease occurrence. ILPD dataset was used to analyze the performance of the model. The predictive model has shown outstanding performance with a prediction accuracy rate of 72.4%, the sensitivity of 90.3%, the specificity of 78.3 %, Type I Error of 9.7 %, Type II Error of 21.7 %, and ROC of 0.758%. The model has furthermore confirmed the feasibility of the laboratory tests such as as (Age; Direct Bilirubin (DB), Alamine_Aminotransferase (SGPT), Total_Protiens (TP), Albumin (ALB)) to predict the disease progression. The predictive model will be helpful to patients and doctors to realize the progression of the disease and make a suitable timely intervention.

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Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis

Cited by 102 publications

References 39 publications

Radiomics and Deep Learning: Hepatic Applications

Radiomics and Deep Learning: Hepatic Applications

Application of Artificial Intelligence in Chronic Liver Diseases: A Systematic Review and Meta-analysis

A Predictive model for liver disease progression based on logistic regression algorithm

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