背景和研究目的胰腺炎是经内镜下放置自膨式金属支架(SEMS)治疗恶性胆道梗阻(MBO)的潜在致命不良事件。在这种情况下,基于深度学习的图像识别在预测胰腺炎方面尚未得到研究。患者和方法我们包括70例接受内镜下放置SEMS治疗不可切除的远端MBO的患者。我们使用一系列覆盖整个胰腺的术前计算机断层扫描图像(总共≥120,960张增强图像)构建了用于胰腺炎预测的卷积神经网络(CNN)模型。我们检查了基于CNN的概率对以下基于临床参数的机器学习模型的额外影响:逻辑回归,具有线性或RBF核的支持向量机,随机森林分类器,和梯度增强分类器。模型性能基于接收器工作特性分析中的曲线下面积(AUC)进行评估,阳性预测值(PPV),准确度,和特异性。结果CNN模型与中等水平的性能指标相关:AUC,0.67;PPV,0.45;精度,0.66;和特异性,0.63.当添加到机器学习模型时,基于CNN的概率提高了性能指标。具有基于CNN的概率的逻辑回归模型的AUC为0.74,PPV为0.85,准确性为0.83,特异性为0.96,而分别为0.72、0.78、0.77和0.96。没有概率。结论基于CNN的模型可以提高内镜下放置胆道SEMS后胰腺炎的可预测性。我们的研究结果支持深度学习技术在胰胆管内镜治疗中改善预后模型的潜力。
Background and study aims Pancreatitis is a potentially lethal adverse event of endoscopic transpapillary placement of a self-expandable metal stent (SEMS) for malignant biliary obstruction (MBO). Deep learning-based image recognition has not been investigated in predicting pancreatitis in this setting. Patients and methods We included 70 patients who underwent endoscopic placement of a SEMS for nonresectable distal MBO. We constructed a convolutional neural network (CNN) model for pancreatitis prediction using a series of pre-procedure computed tomography images covering the whole pancreas (≥ 120,960 augmented images in total). We examined the additional effects of the CNN-based probabilities on the following machine learning models based on clinical parameters: logistic regression, support vector machine with a linear or RBF kernel, random forest classifier, and gradient boosting classifier. Model performance was assessed based on the area under the curve (AUC) in the receiver operating characteristic analysis, positive predictive value (PPV), accuracy, and specificity. Results The CNN model was associated with moderate levels of performance metrics: AUC, 0.67; PPV, 0.45; accuracy, 0.66; and specificity, 0.63. When added to the machine learning models, the CNN-based probabilities increased the performance metrics. The logistic regression model with the CNN-based probabilities had an AUC of 0.74, PPV of 0.85, accuracy of 0.83, and specificity of 0.96, compared with 0.72, 0.78, 0.77, and 0.96, respectively, without the probabilities. Conclusions The CNN-based model may increase predictability for pancreatitis following endoscopic placement of a biliary SEMS. Our findings support the potential of deep learning technology to improve prognostic models in pancreatobiliary therapeutic endoscopy.