OBJECTIVES To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading. METHODS Totally 466 patients undergoing partial hepatectomy were enrolled, including 401 with chronic hepatitis B and 65 without fibrosis pathologically. All patients received elastography and got liver stiffness measurement (LSM) 2-3 days before surgery. We proposed a deep convolutional neural network by TL to analyze images of gray scale modality (GM) and elastogram modality (EM). The TL process was used for liver fibrosis classification by Inception-V3 network which pretrained on ImageNet. The diagnostic performance of TL and non-TL was compared. The value of single modalities, including GM and EM alone, and multimodalities, including GM + LSM and GM + EM, was evaluated and compared with that of LSM and serological indexes. Receiver operating characteristic curve analysis was performed to calculate the optimal area under the curve (AUC) for classifying fibrosis of S4, ≥ S3, and ≥ S2. RESULTS TL in GM and EM demonstrated higher diagnostic accuracy than non-TL, with significantly higher AUCs (all p < .01). Single-modal GM and EM both performed better than LSM and serum indexes (all p < .001). Multimodal GM + EM was the most accurate prediction model (AUCs are 0.950, 0.932, and 0.930 for classifying S4, ≥ S3, and ≥ S2, respectively) compared with GM + LSM, GM and EM alone, LSM, and biomarkers (all p < .05). CONCLUSIONS Liver fibrosis can be staged by a transfer learning modal based on the combination of gray scale and elastogram ultrasound images, with excellent performance. KEY POINTS • Transfer learning consists in applying to a specific deep learning algorithm that pretrained on another relevant problem, expected to reduce the risk of overfitting due to insufficient medical images. • Liver fibrosis can be staged by transfer learning radiomics with excellent performance. • The most accurate prediction model of transfer learning by Inception-V3 network is the combination of gray scale and elastogram ultrasound images.

中文翻译：

---

基于多模式超声成像的转移学习放射学用于肝纤维化分期。

目的提出一种转移学习（TL）放射学模型，该模型可以有效地结合灰度和弹性成像超声图像中的信息，以进行准确的肝纤维化分级。方法纳入466例行部分肝切除术的患者，包括401例慢性乙型肝炎和65例在病理上无纤维化的患者。所有患者在手术前2-3天接受弹性成像并进行肝硬度测量（LSM）。我们通过TL提出了一个深度卷积神经网络，以分析灰度模态（GM）和弹性图模态（EM）的图像。TL过程通过ImageNet上预训练的Inception-V3网络用于肝纤维化分类。比较了TL和非TL的诊断性能。单一模式（包括单独的GM和EM）以及多种模式（包括GM + LSM和GM + EM）的价值，被评估并与LSM和血清学指标进行比较。进行受试者工作特征曲线分析以计算曲线下的最佳面积（AUC），以对S4，≥S3和≥S2的纤维化进行分类。结果GM和EM中的TL显示出比非TL高的诊断准确性，并且AUC显着更高（所有p <.01）。单模态GM和EM均优于LSM和血清指标（所有p <0.001）。与GM + LSM，单独GM和EM，LSM和生物标记物相比，多模式GM + EM是最准确的预测模型（对S4，≥S3和≥S2进行分类的AUC分别为0.950、0.932和0.930） <.05）。结论肝纤维化可以通过基于灰度和弹性成像超声图像相结合的转移学习模式进行，其表现出色。要点•转移学习包括应用针对另一个相关问题进行了预训练的特定深度学习算法，以减少由于医学图像不足而导致过度拟合的风险。•肝纤维化可以通过转移学习放射学表现出色。•Inception-V3网络最准确的转移学习预测模型是灰度图像和弹性成像超声图像的组合。