PDF(Pubmed)
Abstract：
UNASSIGNED: First-line surveillance on hepatitis B virus (HBV)-infected populations with B-mode ultrasound is relatively limited to identifying hepatocellular carcinoma (HCC) without elevated α-fetoprotein (AFP). To improve the present HCC surveillance strategy, the state of the art of artificial intelligence (AI), a deep learning (DL) approach, is proposed to assist in the diagnosis of a focal liver lesion (FLL) in HBV-infected liver background.
UNASSIGNED: Our proposed deep learning model was based on B-mode ultrasound images of surgery that proved 209 HCC and 198 focal nodular hyperplasia (FNH) cases with 413 lesions. The model cohort and test cohort were set at a ratio of 3:1, in which the test cohort was composed of AFP-negative HBV-infected cases. Four additional deep learning models (MobileNet, Resnet50, DenseNet121, and InceptionV3) were also constructed as comparative baselines. To evaluate the models in terms of diagnostic power, sensitivity, specificity, accuracy, confusion matrix, F1-score, and area under the receiver operating characteristic curve (AUC) were calculated in the test cohort.
UNASSIGNED: The AUC of our model, Xception, achieved 93.68% in the test cohort, superior to other baselines (89.06%, 85.67%, 83.94%, and 78.13% respectively for MobileNet, Resnet50, DenseNet121, and InceptionV3). In terms of diagnostic power, our model showed sensitivity, specificity, accuracy, and F1-score of 96.08%, 76.92%, 86.41%, and 87.50%, respectively, and PPV, NPV, FPR, and FNR calculated from the confusion matrix were respectively 80.33%, 95.24%, 23.08%, and 3.92% in identifying AFP-negative HCC from HBV-infected FLL cases. Satisfactory robustness of our proposed model was shown based on 5-fold cross-validation performed among the models above.
UNASSIGNED: Our DL approach has great potential to assist B-mode ultrasound in identifying AFP-negative HCC from FLL found in surveillance of HBV-infected patients.
UNASSIGNED: Our proposed deep learning model was based on B-mode ultrasound images of surgery that proved 209 HCC and 198 focal nodular hyperplasia (FNH) cases with 413 lesions. The model cohort and test cohort were set at a ratio of 3:1, in which the test cohort was composed of AFP-negative HBV-infected cases. Four additional deep learning models (MobileNet, Resnet50, DenseNet121, and InceptionV3) were also constructed as comparative baselines. To evaluate the models in terms of diagnostic power, sensitivity, specificity, accuracy, confusion matrix, F1-score, and area under the receiver operating characteristic curve (AUC) were calculated in the test cohort.
UNASSIGNED: The AUC of our model, Xception, achieved 93.68% in the test cohort, superior to other baselines (89.06%, 85.67%, 83.94%, and 78.13% respectively for MobileNet, Resnet50, DenseNet121, and InceptionV3). In terms of diagnostic power, our model showed sensitivity, specificity, accuracy, and F1-score of 96.08%, 76.92%, 86.41%, and 87.50%, respectively, and PPV, NPV, FPR, and FNR calculated from the confusion matrix were respectively 80.33%, 95.24%, 23.08%, and 3.92% in identifying AFP-negative HCC from HBV-infected FLL cases. Satisfactory robustness of our proposed model was shown based on 5-fold cross-validation performed among the models above.
UNASSIGNED: Our DL approach has great potential to assist B-mode ultrasound in identifying AFP-negative HCC from FLL found in surveillance of HBV-infected patients.
摘要：
■使用B型超声对乙型肝炎病毒（HBV）感染人群的一线监测相对限于识别肝细胞癌（HCC）而没有升高的甲胎蛋白（AFP）。为了改进目前的HCC监测策略，人工智能(AI)的最新技术，深度学习(DL)方法，建议协助在HBV感染的肝脏背景下诊断局灶性肝脏病变（FLL）。
■我们提出的深度学习模型基于手术的B型超声图像，该图像证明了209例HCC和198例局灶性结节增生（FNH）病例，有413个病灶。模型队列和测试队列设置为3：1的比例，其中测试队列由AFP阴性HBV感染病例组成。四个额外的深度学习模型(MobileNet、Resnet50，DenseNet121和InceptionV3）也被构建为比较基线。要根据诊断能力评估模型，灵敏度,特异性，准确度,混淆矩阵，F1分数，在测试队列中计算受试者工作特征曲线下面积(AUC)。
■我们模型的AUC，Xception,在测试队列中达到93.68%，优于其他基线(89.06%，85.67%,83.94%,MobileNet分别为78.13%，Resnet50、DenseNet121和InceptionV3）。在诊断能力方面，我们的模型显示出敏感性，特异性，准确度,F1得分为96.08%，76.92%,86.41%，和87.50%，分别,和PPV,NPV,FPR,根据混淆矩阵计算的FNR分别为80.33%，95.24%,23.08%,从HBV感染的FLL病例中识别AFP阴性HCC的比例为3.92％。基于上述模型之间进行的5倍交叉验证，显示了我们提出的模型的令人满意的鲁棒性。
■我们的DL方法具有很大的潜力，以协助B型超声在HBV感染患者的监测发现从FLLAFP阴性HCC。
■我们提出的深度学习模型基于手术的B型超声图像，该图像证明了209例HCC和198例局灶性结节增生（FNH）病例，有413个病灶。模型队列和测试队列设置为3：1的比例，其中测试队列由AFP阴性HBV感染病例组成。四个额外的深度学习模型(MobileNet、Resnet50，DenseNet121和InceptionV3）也被构建为比较基线。要根据诊断能力评估模型，灵敏度,特异性，准确度,混淆矩阵，F1分数，在测试队列中计算受试者工作特征曲线下面积(AUC)。
■我们模型的AUC，Xception,在测试队列中达到93.68%，优于其他基线(89.06%，85.67%,83.94%,MobileNet分别为78.13%，Resnet50、DenseNet121和InceptionV3）。在诊断能力方面，我们的模型显示出敏感性，特异性，准确度,F1得分为96.08%，76.92%,86.41%，和87.50%，分别,和PPV,NPV,FPR,根据混淆矩阵计算的FNR分别为80.33%，95.24%,23.08%,从HBV感染的FLL病例中识别AFP阴性HCC的比例为3.92％。基于上述模型之间进行的5倍交叉验证，显示了我们提出的模型的令人满意的鲁棒性。
■我们的DL方法具有很大的潜力，以协助B型超声在HBV感染患者的监测发现从FLLAFP阴性HCC。
