Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network
Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network
复制标题
使用基于深度学习的数据集成网络诊断慢性乙型肝炎患者的显着肝纤维化
DOI：
10.1007/s12072-021-10294-4
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发表时间：
2022-03-21
影响因子：
6.6
通讯作者：
Chen, Xin
中科院分区：
文献类型：
--
作者：
Liu, Zhong;Wen, Huiying;Chen, Xin
背景和目的慢性B型肝炎病毒（CHB）感染仍然是全球主要的健康负担，并且在CHB患者中显著肝纤维化（>= F2）的非侵入性和准确诊断在临床上非常重要。本研究旨在评估在深度学习模型中联合使用肝实质超声图像、肝脏硬度值和患者临床参数的潜力，以改善CHB患者>= F2的诊断。方法在527例接受超声检查、肝脏弹性成像和活检的CHB患者中，纳入284例符合条件的患者。我们开发了一个基于深度学习的数据集成网络（DI-Net），以融合肝实质超声图像的信息，肝脏硬度值和患者的临床参数，用于诊断CHB患者>= F2。DI-Net的性能在纳入患者的主要队列（n=155）中进行了交叉验证，并在独立队列（n=129）中进行了外部验证，与单源数据模型和其他非侵入性方法的受试者工作特征曲线下面积（AUC）进行比较。（95%置信区间[CI] 0.893-0.973），AUC为0.901（95%CI 0.834-0.945），显著高于比较方法（交叉和外部验证的AUC范围分别为0.774-0.877和0.741-0.848，p（s）< 0.01）。结论在深度学习模型中联合使用肝实质超声图像、肝脏硬度值和患者的临床参数可以显著提高CHB患者中>= F2的诊断。[图形]。
Background and aims 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.Methods 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).Results 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(s) < 0.01).Conclusion 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.[GRAPHICS].