National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, People's Republic of China.
First Medical College of Guangdong Medical University, Zhanjiang, People's Republic of China.
Hepatol Int. 2022 Jun;16(3):526-536. doi: 10.1007/s12072-021-10294-4. Epub 2022 Mar 21.
Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥ F2) in CHB patients is clinically very important. This study aimed to assess the potential of the joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model to improve the diagnosis of ≥ F2 in CHB patients.
Of 527 CHB patients who underwent US examination, liver elastography and biopsy, 284 eligible patients were included. We developed a deep learning-based data integration network (DI-Net) to fuse the information of ultrasound images of liver parenchyma, liver stiffness values and patients' clinical parameters for diagnosing ≥ F2 in CHB patients. The performance of DI-Net was cross-validated in a main cohort (n = 155) of the included patients and externally validated in an independent cohort (n = 129), with comparisons against single-source data-based models and other non-invasive methods in terms of the area under the receiver-operating-characteristic curve (AUC).
DI-Net achieved an AUC of 0.943 (95% confidence interval [CI] 0.893-0.973) in the cross-validation, and an AUC of 0.901 (95% CI 0.834-0.945) in the external validation, which were significantly greater than those of the comparative methods (AUC ranges: 0.774-0.877 and 0.741-0.848 for cross- and external validations, respectively, p < 0.01).
The joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model could significantly improve the diagnosis of ≥ F2 in CHB patients.
慢性乙型肝炎病毒（CHB）感染仍是全球重大健康负担，准确诊断 CHB 患者显著肝纤维化（≥F2）具有重要的临床意义。本研究旨在评估联合应用肝脏实质超声图像、肝硬度值和患者临床参数的深度学习模型在提高 CHB 患者≥F2 诊断中的潜力。
对 527 例接受超声检查、肝脏弹性成像和肝活检的 CHB 患者进行分析，纳入 284 例符合条件的患者。我们开发了一种基于深度学习的数据集成网络（DI-Net），以融合肝脏实质超声图像、肝硬度值和患者临床参数信息，用于诊断 CHB 患者≥F2。在包含的患者的主要队列（n=155）中对 DI-Net 的性能进行了交叉验证，并在独立队列（n=129）中进行了外部验证，通过与基于单一来源数据的模型和其他非侵入性方法比较，评估了其在接受者操作特征曲线（ROC）下面积（AUC）方面的表现。
DI-Net 在交叉验证中的 AUC 为 0.943（95%置信区间 [CI] 0.893-0.973），在外部验证中的 AUC 为 0.901（95% CI 0.834-0.945），均显著大于比较方法（交叉验证和外部验证的 AUC 范围分别为 0.774-0.877 和 0.741-0.848，p<0.01）。
联合应用肝脏实质超声图像、肝硬度值和患者临床参数的深度学习模型可显著提高 CHB 患者≥F2 的诊断效能。
