Value of multimodal MRI radiomics and machine learning in predicting staging liver fibrosis and grading inflammatory activity.

Overview

abstract

OBJECTIVE: To evaluate the value of radiomics models created based on non-contrast enhanced T₁ weighted (T₁W) and T₂W fat-saturated (T₂WFS) images for staging hepatic fibrosis (HF) and grading inflammatory activity. METHODS AND MATERIALS: Data of 280 patients with pathologically confirmed HF and 48 healthy volunteers were included. The participants were divided into the training set and the test set at the proportion of 4:1 by the random seed method. We used the Pyradiomics software to extract radiomics features, and then use the least absolute shrinkage and selection operator to select the optimal subset. Finally, we used the stochastic gradient descent classifier to build the prediction models. DeLong test was used to compare the diagnostic performance of the models. Receiver operating characteristics was used to evaluate the prediction ability of the models. RESULTS: The diagnostic efficiency of the models based on T₁W & T₂WFS images were the highest (all p < 0.05). When discriminating significant fibrosis (≥ F2), there were significant differences in the AUCs between the machine learning models based on T₁W and T₂WFS images (p < 0.05), but there were no significant differences in area under the receiver operating characteristic curves between the two models in other groups (all p > 0.05). CONCLUSION: The radiomics models built on T₁W and T₂WFS images are effective in assessing HF and inflammatory activity. ADVANCES IN KNOWLEDGE: Based on conventional MR sequences that are readily available in the clinic, namely unenhanced T₁W and T₂W images. Radiomics can be used for diagnosis and differential diagnosis of liver fibrosis staging and inflammatory activity grading.