Purpose

The early diagnosis of liver fibrosis is crucial for the prevention of liver cirrhosis and liver cancer. As gold standard for staging liver fibrosis, liver biopsy is an invasive procedure that carries the risk of serious complications. The aim of this study was to evaluate the performance of the residual neural network (ResNet), a non-invasive methods, for staging liver fibrosis using plain CT images.

Methods

This retrospective study involved 347 patients subjected to liver CT scanning and liver biopsy. For each patient, we selected three axial images adjacent to the puncture location in the eighth or ninth inter-space on the right side. After processing and enhancement (rotation, translation, and amplification), these images were used as input data for the ResNet model. The model used a fivefold cross-validation method. In each fold, the images of approximately 80% of the total sample size (278 patients) were used for training the ResNet model, the other 20% (69 patients) were used for testing the trained network, with the liver biopsy pathology results as gold standard. The proportion of patients in each fibrosis stage was equal for training and test groups. The final result was the mean of the fivefold cross-validation in the test group. The performance of the ResNet model was evaluated for the test group by receiver operating characteristic (ROC) analysis.

Results

For the ResNet model, the area under the ROC curve (AUC) for assessing cirrhosis (F4), advanced fibrosis (F3 or higher), significant fibrosis (F2 or higher), and mild fibrosis (F1 or higher) was 0.97, 0.94, 0.90, and 0.91, respectively.

Conclusions

The ResNet model analysis of plain CT images exhibited high diagnostic efficiency for liver fibrosis staging. As a convenient, fast, and economical non-invasive diagnostic method, the ResNet model can be used to assist radiologists and clinicians in liver fibrosis evaluations.

中文翻译：

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深层残留网模型可在普通CT图像上进行肝纤维化分期。

目的

肝纤维化的早期诊断对于预防肝硬化和肝癌至关重要。作为分期肝纤维化的金标准，肝活检是一种侵入性手术，具有严重并发症的风险。这项研究的目的是评估残余神经网络（ResNet）（一种非侵入性方法）使用普通CT图像进行肝纤维化分期的性能。

方法

这项回顾性研究涉及347例接受了肝脏CT扫描和肝活检的患者。对于每位患者，我们在右侧的第八个或第九个间隙中选择了与穿刺位置相邻的三个轴向图像。经过处理和增强（旋转，平移和放大）后，这些图像用作ResNet模型的输入数据。该模型使用五重交叉验证方法。在每个折叠中，约占总样本量的80％（278例）的图像用于训练ResNet模型，其余20％（69例）的图像用于测试训练后的网络，肝脏活检病理结果为黄金标准。对于每个纤维化阶段，培训组和测试组的患者比例是相等的。最终结果是测试组中五重交叉验证的平均值。

结果

对于ResNet模型，用于评估肝硬化（F4），晚期纤维化（F3或更高），显着纤维化（F2或更高）和轻度纤维化（F1或更高）的ROC曲线（AUC）下的面积分别为0.97、0.94，分别为0.90和0.91。

结论

普通CT图像的ResNet模型分析显示出对肝纤维化分期的高诊断效率。作为一种方便，快速且经济的无创诊断方法，ResNet模型可用于协助放射科医生和临床医生进行肝纤维化评估。