Abstract
ObjectiveTo assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.Materials and MethodsThis 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).ResultsORF 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).ConclusionMachine-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.
Highlights
The early detection and accurate staging of liver significant fibrosis are crucial for antiviral therapy
We present the concept of multiparametric ultrasomics, which is a machine learning-based clinical decision support system that uses US imaging big data
The results showed that the AdaBoost, random forest (RF) and support vector machine (SVM) classifiers outperformed the other classifiers
Summary
The early detection and accurate staging of liver significant fibrosis are crucial for antiviral therapy. We present the concept of multiparametric ultrasomics, which is a machine learning-based clinical decision support system that uses US imaging big data. According to our preliminary and reported studies [10, 11, 19,20,21], the three categories of parameters (conventional radiomics, ORF and CEMF features) acquired from the three types of images were expected to provide potential information for liver fibrosis staging and are detailed in the Supplementary Materials. Six models with different machine-learning methods were built on the training data set, and the performance of each model was assessed on the validation data set. The classification performance for significant fibrosis was assessed using the AUC in the validation data sets. All statistical tests were two-sided, and p values < 0.05 were considered statistically significant
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