深度学习放射学模型可准确预测慢性乙型肝炎患者肝细胞癌

StephenW

Deep learning radiomics model accurately predicts hepatocellular carcinoma occurrence in chronic hepatitis B patients: a five-year follow-up
Jieyang Jin  1   2 , Zhao Yao  3 , Ting Zhang  1   2 , Jie Zeng  1   2 , Lili Wu  1   2 , Manli Wu  1   2 , Jinfen Wang  1   2 , Yuanyuan Wang  3   4 , Jinhua Yu  3   4 , Rongqin Zheng  1   2
Affiliations
Affiliations

    1
    Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-Sen University 600 Tianhe Road, Guangzhou, China.
    2
    Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-Sen University 600 Tianhe Road, Guangzhou, China.
    3
    Department of Electronic Engineering, Fudan University Shanghai, China.
    4
    The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai China.

    PMID: 33575088 PMCID: PMC7868753

Abstract

An early and accurate prediction of hepatocellular carcinoma (HCC) is beneficial for individualized treatment and follow-up of chronic hepatitis B (CHB) patients. We aimed to establish a prediction model for HCC by radiomics analysis in CHB patients and compare performance with liver stiffness measurement (LSM) and other clinical prognostic scores. Initially, 1215 patients were included and finally 434 CHB patients with 5-year follow-up were enrolled, 96.3% of them underwent liver biopsy. Deep learning radiomics analysis was performed on 2170 two-dimensional shear wave elastography (2D-SWE) and corresponding B-mode ultrasound (US) images. These high-throughput imaging features were also combined with low-dimensional serological clinical data by deep learning radiomics to establish different HCC prediction models and to overcome challenges of an unbalanced sample. The best model which is simple with high accuracy was selected. Prediction performance of the selected model was compared with LSM and other clinical prognostic scores. During 5-year follow-up, 32 (7.4%) of 434 patients developed HCC. The best prediction model was HCC-R, which included 2D-SWE and B-mode US images, sex and age. This model showed a high predictive value with areas under the receiver operating characteristic curve (AUCs) of 0.981, 0.942 and 0.900 in training, validation and testing cohorts for predicting 5-year prognosis of HCC. These predictive values were significantly higher than that of LSM (AUC: 0.676~0.784, p < 0.05) and better than that of other clinical prognostic scores (AUC: 0.544~0.869). HCC-R radiomics model based on 2D-SWE and B-mode US images, sex and age comprehensively reflected biomechanical and morphological information of patients and can accurately predict HCC occurrence; thus, this model has great value for treatment and follow-up of CHB patients.

Keywords: Radiomics; elastography; hepatocellular carcinoma; prediction; ultrasound.

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StephenW

深度学习放射学模型可准确预测慢性乙型肝炎患者肝细胞癌的发生：五年随访
揭阳金1 2，赵瑶3，张婷1 2，曾杰1 2，吴丽丽1 2，吴曼丽1 2，王金芬1 2，王媛媛3 4，金华玉3 4，荣勤正1 2
隶属关系
隶属关系

    1个
    中山大学附属第三医院超声科，广东省广州市天河路600号
    2
    中山大学附属第三医院广东省肝病研究重点实验室，广州市天河路600号
    3
    上海复旦大学电子工程系
    4
    中国医学影像计算与计算机辅助干预重点实验室。

    PMID：33575088 PMCID：PMC7868753

抽象的

肝细胞癌（HCC）的早期准确预测有助于慢性乙型肝炎（CHB）患者的个体化治疗和随访。我们旨在通过CHB患者的放射组学分析建立HCC预测模型，并将其与肝硬度测量（LSM）和其他临床预后评分进行比较。最初纳入了1215例患者，最后纳入434位CHB患者并进行了5年随访，其中96.3％的患者接受了肝活检。在2170个二维剪切波弹性成像（2D-SWE）和相应的B型超声（US）图像上进行了深度学习放射学分析。这些高通量成像功能还通过深度学习放射学与低维血清学临床数据相结合，以建立不同的HCC预测模型并克服不平衡样品的挑战。选择了简单，高精度的最佳模型。将所选模型的预测性能与LSM和其他临床预后评分进行比较。在5年的随访期间，434例患者中有32例（7.4％）发生了HCC。最好的预测模型是HCC-R，其中包括2D-SWE和B模式的US图像，性别和年龄。该模型在训练，验证和测试队列中预测HCC的5年预后时，在接受者工作特征曲线（AUC）下的面积分别为0.981、0.942和0.900，显示出较高的预测价值。这些预测值显着高于LSM（AUC：0.676〜0.784，p <0.05），并且优于其他临床预后评分（AUC：0.544〜0.869）。基于2D-SWE和B型US图像，性别和年龄的HCC-R放射学模型全面反映了患者的生物力学和形态学信息，可以准确预测HCC的发生；因此，该模型对CHB患者的治疗和随访具有重要价值。

关键字：Radiomics；弹性成像肝细胞癌；预言;超声。

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