Ultrasound radiomics-based logistic regression model for fibrotic NASH.

Abstract

Background: Those who have severe fibrosis (F2 ≥ 2 stage) are at the greatest risk for the advancement of the illness among non-alcoholic fatty liver patients. To forecast the non-alcoholic steatohepatitis (NASH) probability accompanied by significant fibrosis, we propose to develop and validate a nomogram liver imaging reporting and data system, providing robust evidence for preventing and treating clinical liver diseases.

Methods: The study used SD rats to create a model of hepatic steatosis and fibrosis by feeding them a high-fat diet and injecting Ccl4 subcutaneously. Radiomics characteristics were derived from two-dimensional liver ultrasound images of the rats, and a radiomics model was constructed, with rad-scores calculated accordingly. Univariate and multivariate logistic regression was employed to ascertain the clinical characteristics of rats and liver elasticity values, aiming to establish a clinical model. Ultimately, a clinical radiomics model was created by integrating the rad-score from the radiomics model with independent clinical characteristics from the clinical model. A forest plot was generated to depict this integration. The forest plot's performance was assessed by the use of the area under the receiver operating characteristic (ROC) curve (AUC), decision curve analysis, and calibration curve.

Results: The areas under the receiver operating characteristic curve (AUC) for the training set and validation set of the clinical radiomics model were 0.986 and 0.971, respectively. Decision curve analysis showed that the clinical radiomics model had the highest net benefit across most threshold probability ranges.

Conclusion: The nomogram and clinical radiomics model, which consists of clinical characteristics, real-time shear wave elastography, and radiomics, provide excellent predictive capability in assessing the likelihood of fibrotic NASH.