Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review

Hemant Goyal; Syed Ali Amir Sherazi; Shweta Gupta; Abhilash Perisetti; Ikechukwu Achebe; Aman Ali; Benjamin Tharian; Nirav Thosani; Neil R Sharma

doi:10.1177/17562848221093873

Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review

Therap Adv Gastroenterol. 2022 Apr 29:15:17562848221093873. doi: 10.1177/17562848221093873. eCollection 2022.

Authors

Hemant Goyal¹, Syed Ali Amir Sherazi², Shweta Gupta³, Abhilash Perisetti⁴, Ikechukwu Achebe², Aman Ali⁵, Benjamin Tharian⁶, Nirav Thosani⁷, Neil R Sharma⁸

Affiliations

¹ The Wright Center for Graduate Medical Education, 501 S. Washington Avenue, Scranton, PA 18503, USA.
² Department of Medicine, John H. Stroger J.r Hospital of Cook County, Chicago, IL, USA.
³ Division of Hematology-Oncology, John H. Stroger Jr. Hospital of Cook County, Chicago, IL, USA.
⁴ Advanced Interventional Oncology and Surgical Endoscopy, Parkview Cancer Institute, Parkview Health, Fort Wayne, IN, USA.
⁵ Commonwealth Medical College, Scranton, PA, USA; Wilkes Barre General Hospital, Wilkes-Barre, PA, USA; Digestive Care Associates, Kingston, PA, USA.
⁶ University of Arkansas for Medical Sciences, Little Rock, AR, USA.
⁷ Center for Interventional Gastroenterology at UTHealth (iGUT), Houston, TX, USADivision of Gastroenterology, Hepatology & Nutrition, McGovern Medical School, UTHealth, Houston, TX, USA.
⁸ Division of interventional Oncology & Surgical Endoscopy (IOSE), Parkview cancer Institute, Fort Wayne, IN, USAIndiana University School of Medicine, Fort Wayne, IN, USA.

Abstract

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.

Keywords: artificial intelligence; artificial neural network; convolutional neural network; endoscopic ultrasonography; pancreatic cancer; support vector machine.

Publication types

Review