Li X X, Cheng G W, Liang J, Huang C, Qiu L P, Ding H
Shanghai Institute of Medical Imaging, Shanghai 200032, China.
Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai 200040, China.
Zhonghua Yi Xue Za Zhi. 2023 Aug 8;103(29):2246-2251. doi: 10.3760/cma.j.cn112137-20221213-02641.
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.
探讨剪切波频散（SWD）和剪切波弹性成像（SWE）联合血清学指标在肝纤维化评估中的应用价值。2021年1月至2022年9月，前瞻性收集复旦大学附属华山医院219例接受肝穿刺活检的肝病患者，其中男性130例，女性89例，年龄18～76（42±12）岁。所有患者在肝穿刺活检前行SWD和SWE检查，并收集血清学指标，包括丙氨酸氨基转移酶（ALT）、天冬氨酸氨基转移酶（AST）、碱性磷酸酶（ALP）和γ-谷氨酰转肽酶（GGT）。根据肝纤维化病理分期（S0～S4），分析所有患者不同纤维化分期的频散斜率和肝脏弹性模量分布。所有患者按时间顺序7∶3分为训练集（156例）和验证集（63例）。在训练集中，采用二元logistic回归分析影响肝纤维化≥S2期和S4期的因素。利用R语言建立诊断肝纤维化≥S2期和S4期的预测模型，并通过曲线下面积（AUC）进行评估，同时进行校准验证。频散斜率和弹性模量随纤维化程度加重而升高，不同纤维化分期差异有统计学意义（均P<0.001）。在训练集中，频散斜率、弹性模量、ALT、AST和GGT是肝纤维化≥S2期和S4期的影响因素（均P<0.05），并基于这些指标构建预测模型。在训练集中，预测模型、SWD和SWE诊断肝纤维化≥S2期的AUC分别为0.743（95%CI：0.665～0.821）、0.709（95%CI：0.628～0.790）和0.725（95%CI：0.647～0.804）；诊断肝纤维化S4期的AUC分别为0.988（95%CI：0.968～1.000）、0.908（95%CI：0.852～0.963）和0.974（95%CI：0.945～1.000）。在验证集中，预测模型、SWD和SWE诊断肝纤维化≥S2期的AUC分别为0.735（95%CI：0.612～0.859）、0.658（95%CI：0.522～0.793）和0.699（95%CI：0.570～0.828）；诊断肝纤维化S4期的AUC分别为0.976（95%CI：0.937～1.000）、0.872（95%CI：0.7～0.988）和0.948（95%CI：0.889～1.000）。预测模型的校准曲线在训练集和验证集中一致。SWD和SWE联合血清学指标的预测模型有助于无创诊断肝纤维化分期。
