[The application value of shear wave dispersion and shear wave elastography combined with serological indicators in the evaluation of liver fibrosis].

To explore the application value of shear wave dispersion (SWD) and shear wave elastography (SWE) combined with serological indicators in the evaluation of liver fibrosis. A total of 219 patients with liver disorders who underwent liver biopsy were prospectively collected in Huashan Hospital, Fudan University from January 2021 to September 2022, including 130 males and 89 females, aged from 18 to 76 (42±12) years. All patients underwent SWD and SWE examinations before liver biopsy. Serological indicators including alanine aminotransferase(ALT), aspartate aminotransferase(AST), alkaline phosphatase(ALP)) and γ-glutamyl transpeptadase (GGT) were also collected. Based on pathological diagnosis of liver fibrosis stage (from S0 to S4), the distribution of dispersion slope and liver elastic modulus at different fibrosis stages were analyzed in all patients. All patients were divided 7: 3 into training set (156 cases) and validation set (63 cases) in chronological order. In training set, factors influencing liver fibrosis≥S2 stage and S4 stage were analysed using binary logistic regression. The predictive models were established for diagnosing liver fibrosis≥S2 stage and S4 stage by using R language, and the models were evaluated by the area under curve (AUC) and calibrated for validation. The dispersion slope and elastic modulus increased with the severity of fibrosis, with statistically significant differences in different fibrosis stages (both <0.001). In training set, dispersion slope, elastic modulus, ALT, AST, and GGT were influential factors in liver fibrosis≥S2 stage and S4 stage(both <0.05), and prediction models were constructed based on these indicators. In training set, the AUCs of the predictive model, SWD and SWE for diagnosingliver fibrosis≥S2 stage were 0.743 (95%: 0.665-0.821), 0.709 (95%: 0.628-0.790) and 0.725 (95%: 0.647-0.804), respectively; for diagnosing liver fibrosis S4 stage, the AUCs were 0.988 (95%: 0.968-1.000), 0.908 (95%: 0.852-0.963) and 0.974 (95%: 0.945-1.000), respectively. In validation set, the AUC of the predictive model, SWD and SWE for diagnosing liver fibrosis≥S2 stage were 08.735 (95%: 0.612-0.859), 0.658 (95%:0.522-0.793) and 0.699 (95%:0.570-0.828), respectively; for diagnosing liver fibrosis S4 stage, the AUC were 0.976 (95%: 0.937-1.000), 0.872 (95%: 0.757-0.988) and 0.948 (95%: 0.889-1.000), respectively. The calibration curves of the prediction models were consistent in the training and validation sets. The predictive model of SWD and SWE combined with serological indicators is helpful in the diagnosis of stage of liver fibrosis non-invasively.