Dataset Information

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

ABSTRACT:

Objective

To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.

Materials and methods

This prospective study consisted of 144 patients with chronic hepatitis B. Ultrasomics-high-throughput quantitative data from ultrasound imaging of liver fibrosis-were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlation and hierarchical clustering. Features were selected using diagnostic tests for fibrosis, activity and steatosis stage, with the histopathological results as the reference. The fibrosis staging performance of ultrasomics models with combinations of the selected features was evaluated with machine-learning algorithms by calculating the area under the receiver-operator characteristic curve (AUC).

Results

ORF and CEMF features had better predictive power than conventional radiomics for liver fibrosis stage (both p < 0.01). CEMF features exhibited the highest diagnostic value for activity stage (both p < 0.05), and ORF had the best diagnostic value for steatosis stage (both p < 0.01). The machine-learning classifiers of adaptive boosting, random forest and support vector machine were found to be optimal algorithms with better (all mean AUCs = 0.85) and more stable performance (coefficient of variation = 0.01-0.02) for fibrosis staging than decision tree, logistic regression and neural network (mean AUC = 0.61-0.72, CV = 0.07-0.08). The multiparametric ultrasomics model achieved much better performance (mean AUC values of 0.78-0.85) than the features from a single modality in discriminating significant fibrosis (≥ F2).

Conclusion

Machine-learning-based analysis of multiparametric ultrasomics can help improve the discrimination of significant fibrosis compared with mono or dual modalities.

Key points

• Multiparametric ultrasomics has achieved much better performance in the discrimination of significant fibrosis (≥ F2) than the single modality of conventional radiomics, original radiofrequency and contrast-enhanced micro-flow. • Adaptive boosting, random forest and support vector machine are the optimal algorithms for machine learning.

SUBMITTER: Li W

PROVIDER: S-EPMC6510867 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Li Wei W Huang Yang Y Zhuang Bo-Wen BW Liu Guang-Jian GJ Hu Hang-Tong HT Li Xin X Liang Jin-Yu JY Wang Zhu Z Huang Xiao-Wen XW Zhang Chu-Qing CQ Ruan Si-Min SM Xie Xiao-Yan XY Kuang Ming M Lu Ming-De MD Chen Li-Da LD Wang Wei W

European radiology 20180903 3

<h4>Objective</h4>To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.<h4>Materials and methods</h4>This prospective study consisted of 144 patients with chronic hepatitis B. Ultrasomics-high-throughput quantitative data from ultrasound imaging of liver fibrosis-were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlatio ...[more]

PMID: 30178143

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Dataset Information

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

Objective

Materials and methods

Results

Conclusion

Key points

Publications

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

Similar Datasets

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