Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology

Schawkat, Khoschy; Ciritsis, Alexander; Ulmenstein, Sophie von; Honcharova-Biletska, Hanna; Jüngst, Christoph; Weber, Achim; Gubler, Christoph; Mertens, Joachim C.; Reiner, Caecilia S.

doi:10.1007/s00330-020-06831-8

Cited by 47 publications

(32 citation statements)

References 42 publications

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“…Recently, Schawkat et al showed that PCA‐SVM with texture analysis features of T 1 ‐weighted images could differentiate low‐stage fibrosis (F0–2) from advanced fibrosis stage (F3–4) with an average accuracy of 85.7% 27 . For discriminating between F0–2 and F3–4, our study showed that LASSO‐SVM and PCA‐SVM had the same accuracy of 88.24% and the same AUC of 0.93.…”

Section: Discussionsupporting

confidence: 56%

Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T₁‐Weighted Imaging: Comparison of Different Radiomics Models

Wang

et al. 2020

Magnetic Resonance Imaging

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Background Liver fibrosis is a common process resulting from various etiologies. Sustained progression of liver fibrosis leads to cirrhosis, even hepatocellular carcinoma. Thus, noninvasive staging of liver fibrosis is of clinical importance. Radiomics is an emerging approach for staging liver fibrosis. However, the feature selection methods and classifier models are complicated, and may result in a discrepancy of diagnostic performance owing to different radiomics models. Purpose To identify the optimal feature selection and classifier methods for predicting liver fibrosis by using nonenhanced T1‐weighted imaging. Study Type Prospective. Animal Model Wistar rats, total 97. Field Strength/Sequence 3T, 3D T1‐weighted images with fast‐spoiled gradient echo (FSPGR). Assessment Liver fibrosis rats were induced via subcutaneous injection of a mixture of carbon tetrachloride. Rats in the control group were injected with saline. Segmentation and feature extraction were performed by 3D slicer and the image biomarker explorer (IBEX) software package. Data preprocessing, feature selection, model building, and model comparative evaluation were conducted with Python. The liver fibrosis stage was determined by pathological examination. Statistical Tests Receiver operating characteristic curve, fuzzy comprehensive evaluation. Results For discriminating between F0 and F1‐2, F0 and F3‐4, F0 and F1‐4, F0‐1 and F2‐4, F0‐2 and F3‐4, and F0‐3 and F4, the accuracies of 12 radiomics models were 77.27–90.91%, 73.33–86.67%, 80.56–91.67%, 74.07–88.89%, 76.47–88.24%, and 79.49–92.31%, respectively. The AUCs of the radiomics models were 0.86–0.97, 0.85–0.95, 0.89–0.97, 0.81–0.96, 0.82–0.93, and 0.85–0.96, respectively. The least absolute shrinkage and selection operator / support vector machine (LASSO‐SVM) model had high AUCs of 0.93–0.97. For discriminating between F0 and F1‐2, F0 and F3‐4, F0 and F1‐4, F0‐1 and F2‐4, and F0‐2 and F3‐4, the fuzzy comprehensive evaluation showed that the LASSO‐SVM model had a high fuzzy score/order of 0.087–0.091/1. Data Conclusion LASSO‐SVM appears to be the optimal model for predicting liver fibrosis by using nonenhanced T1‐weighted imaging in a rodent model of liver fibrosis. Level of Evidence 2. Technical Efficacy Stage 2.

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Section: Discussionsupporting

confidence: 56%

Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T₁‐Weighted Imaging: Comparison of Different Radiomics Models

Wang

et al. 2020

Magnetic Resonance Imaging

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“…e possible reason may be the attenuation of the fat signal in out-of-phase T1W images and it is interesting to note that steatosis, which shows diffusely decreased liver attenuation, is also one of the features of chronic liver disease. Some studies have shown that TA extracted from T1W images has an excellent performance in the classification of liver fibrosis [10,11]. However, our study may be the first to explore TA from out-of-phase T1W images to classify early-stage fibrosis and to obtain better AUC results compared to TA from in-phase T1W and T2W images.…”

Section: Discussionmentioning

confidence: 88%

“…ese texture features can be extracted through machine learning software and can reflect the extent of heterogeneity, granularity, randomness, and so forth, which may be associated with histopathological changes and contribute to the differential diagnosis and assessment of the development stage of fibrosis. Several promising studies have reported that texture analysis based on MR images can be used for the classification of liver fibrosis [10,11], especially in advanced fibrosis and significant cirrhosis. Few studies have reported methods of classification for the early stages of liver fibrosis, for early detection and subsequent early treatment that can help prevent its progression and ultimately reduce the occurrence of complications related to chronic liver disease.…”

Section: Introductionmentioning

confidence: 99%

Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis

Zhao

Gong

Zhao

et al. 2021

Canadian Journal of Gastroenterology and Hepatology

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Purpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and noncontrast MRI scans, were enrolled. TA parameters were extracted from out-of-phase T1-weighted (T1W), in-phase T1W, and T2-weighted (T2W) images and calculated using Artificial Intelligent Kit (AK). Features were extracted including first-order, shape, gray-level cooccurrence matrix, gray-level run-length matrix, neighboring gray one tone difference matrix, and gray-level differential matrix. After statistical analyses, final diagnostic models were constructed. Receiver operating curves (ROCs) and areas under the ROC (AUCs) were used to assess the diagnostic value of each final model and 100-time repeated cross-validation was applied to assess the stability of the logistic regression models. Results. A total of 57 patients were enrolled in this study, with 27 in the fibrosis stage < 2 and 30 in stages ≥ 2. Overall, 851 features were extracted per ROI. Eight features with high correlation were selected by the maximum relevance method in each sequence, and all had a good diagnostic performance. ROC analysis of the final models showed that all sequences had a preferable performance with AUCs of 0.87, 0.90, and 0.96 in T2W and in-phase and out-of-phase T1W, respectively. Cross-validation results reported the following values of mean accuracy, specificity, and sensitivity: 0.98 each for out-of-phase T1W; 0.90, 0.89, and 0.90 for in-phase T1W; and 0.86, 0.88, 0.84 for T2W in the training set, and 0.76, 0.81, and 0.72 for out-of-phase T1W; 0.74, 0.72, and 0.75 for in-phase T1W; and 0.63, 0.64, and 0.63 for T2W for the test group, respectively. Conclusion. Noncontrast MRI scans with texture analysis are viable for classifying the early stages of liver fibrosis, exhibiting excellent diagnostic performance.

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“…Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis 34 . Besides that, imaging‐based texture analysis‐derived parameter, which is a tool of radiomics, combined with machine learning of non‐contrast‐enhanced T1‐weighted magnetic resonance images could be as accurate (82%) as magnetic resonance elastography for liver fibrosis quantification 35 . These approaches may act as alterative for staging liver fibrosis in different resource settings.…”

Section: Artificial Intelligence and Fibrosismentioning

confidence: 99%

Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis

Wong

Yuen

Andy

et al. 2021

J of Gastro and Hepatol

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Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non‐alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches. In this review article, we discuss the principles of applying AI on electronic health records, liver biopsy, and liver images. A few common AI approaches include logistic regression, decision tree, random forest, and XGBoost for data at a single time stamp, recurrent neural networks for sequential data, and deep neural networks for histology and images.

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Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology

Cited by 47 publications

References 42 publications

Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T₁‐Weighted Imaging: Comparison of Different Radiomics Models

Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T₁‐Weighted Imaging: Comparison of Different Radiomics Models

Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis

Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis

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Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology

Cited by 47 publications

References 42 publications

Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T1‐Weighted Imaging: Comparison of Different Radiomics Models

Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T1‐Weighted Imaging: Comparison of Different Radiomics Models

Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis

Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis

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Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T₁‐Weighted Imaging: Comparison of Different Radiomics Models

Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T₁‐Weighted Imaging: Comparison of Different Radiomics Models