人工智能在内镜超声诊断胰腺恶性肿瘤中的应用：一项系统评价
Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review.
作者信息
Goyal Hemant, Sherazi Syed Ali Amir, Gupta Shweta, Perisetti Abhilash, Achebe Ikechukwu, Ali Aman, Tharian Benjamin, Thosani Nirav, Sharma Neil R
机构信息
The Wright Center for Graduate Medical Education, 501 S. Washington Avenue, Scranton, PA 18503, USA.
Department of Medicine, John H. Stroger J.r Hospital of Cook County, Chicago, IL, USA.
出版信息
Therap Adv Gastroenterol. 2022 Apr 29;15:17562848221093873. doi: 10.1177/17562848221093873. eCollection 2022.
BACKGROUND
Pancreatic cancer (PC) is a highly fatal malignancy with a global overall 5-year survival of under 10%. Screening of PC is not recommended outside of clinical trials. Endoscopic ultrasonography (EUS) is a very sensitive test to identify PC but lacks specificity and is operator-dependent, especially in the presence of chronic pancreatitis (CP). Artificial Intelligence (AI) is a growing field with a wide range of applications to augment the currently available modalities. This study was undertaken to study the effectiveness of AI with EUS in the diagnosis of PC.
METHODS
Studies from MEDLINE and EMBASE databases reporting the AI performance applied to EUS imaging for recognizing PC. Data were analyzed using descriptive statistics. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess the quality of the included studies.
RESULTS
A total of 11 articles reported the role of EUS in the diagnosis of PC. The overall accuracy, sensitivity, and specificity of AI in recognizing PC were 80-97.5%, 83-100%, and 50-99%, respectively, with corresponding positive predictive value (PPV) and negative predictive value (NPV) of 75-99% and 57-100%, respectively. Types of AI studied were artificial neural networks (ANNs), convolutional neural networks (CNN), and support vector machine (SVM). Seven studies using other than basic ANN reported a sensitivity and specificity of 88-96% and 83-94% to differentiate PC from CP. Two studies using SVM reported a 94-96% sensitivity, 93%-99% specificity, and 94-98% accuracy to diagnose PC from CP. The reported sensitivity and specificity of detection of malignant from benign Intraductal Papillary Mucinous Neoplasms (IPMNs) was 96% and 92%, respectively.
CONCLUSION
AI reported a high sensitivity with high specificity and accuracy to diagnose PC, differentiate PC from CP, and differentiate benign from malignant IPMN when used with EUS.
背景
胰腺癌（PC）是一种高度致命的恶性肿瘤，全球总体5年生存率低于10%。除临床试验外，不建议对胰腺癌进行筛查。内镜超声检查（EUS）是识别胰腺癌的一项非常敏感的检查，但缺乏特异性且依赖操作者，尤其是在存在慢性胰腺炎（CP）的情况下。人工智能（AI）是一个不断发展的领域，有广泛的应用来增强目前可用的检查手段。本研究旨在探讨人工智能联合EUS在胰腺癌诊断中的有效性。
方法
检索MEDLINE和EMBASE数据库中报告人工智能应用于EUS成像识别胰腺癌的研究。使用描述性统计方法分析数据。采用诊断准确性研究质量评估（QUADAS - 2）工具评估纳入研究的质量。
结果
共有11篇文章报道了EUS在胰腺癌诊断中的作用。人工智能识别胰腺癌的总体准确率、敏感性和特异性分别为80% - 97.5%、83% - 100%和50% - 99%，相应的阳性预测值（PPV）和阴性预测值（NPV）分别为75% - 99%和57% - 100%。所研究的人工智能类型有人工神经网络（ANN）、卷积神经网络（CNN）和支持向量机（SVM）。七项使用基本ANN以外方法的研究报告，区分胰腺癌与慢性胰腺炎的敏感性和特异性分别为88% - 96%和83% - 94%。两项使用SVM的研究报告，从慢性胰腺炎中诊断胰腺癌的敏感性为94% - 96%，特异性为93% - 99%，准确率为94% - 98%。报告的从良性导管内乳头状黏液性肿瘤（IPMN）中检测恶性肿瘤的敏感性和特异性分别为96%和92%。
结论
当与EUS联合使用时，人工智能在诊断胰腺癌、区分胰腺癌与慢性胰腺炎以及区分良性与恶性IPMN方面具有高敏感性、高特异性和高准确性。
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