Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions

Yusuke Kurita,Shimpei Matsumoto,Atsushi Nakajima,Takamichi Kuwahara,Masahiro Tajika,Nobumasa Mizuno,Yasumasa Niwa,Yasuhiro Shimizu,Hiroki Koda,Masahiro Obata,Kazuo Hara,Nozomi Okuno,Kensuke Kubota

doi:10.1038/s41598-019-43314-3

Yusuke Kurita, Shimpei Matsumoto + Show 11 more

Open Access

https://doi.org/10.1038/s41598-019-43314-3

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Journal: Scientific Reports	Publication Date: May 3, 2019
Citations: 59	License type: open-access

Affiliation: Aichi Cancer Center, Yokohama City University

Abstract

The diagnosis of pancreatic cystic lesions remains challenging. This study aimed to investigate the diagnostic ability of carcinoembryonic antigen (CEA), cytology, and artificial intelligence (AI) by deep learning using cyst fluid in differentiating malignant from benign cystic lesions. We retrospectively reviewed 85 patients who underwent pancreatic cyst fluid analysis of surgical specimens or endoscopic ultrasound-guided fine-needle aspiration specimens. AI using deep learning was used to construct a diagnostic algorithm. CEA, carbohydrate antigen 19-9, carbohydrate antigen 125, amylase in the cyst fluid, sex, cyst location, connection of the pancreatic duct and cyst, type of cyst, and cytology were keyed into the AI algorithm, and the malignant predictive value of the output was calculated. Area under receiver-operating characteristics curves for the diagnostic ability of malignant cystic lesions were 0.719 (CEA), 0.739 (cytology), and 0.966 (AI). In the diagnostic ability of malignant cystic lesions, sensitivity, specificity, and accuracy of AI were 95.7%, 91.9%, and 92.9%, respectively. AI sensitivity was higher than that of CEA (60.9%, p = 0.021) and cytology (47.8%, p = 0.001). AI accuracy was also higher than CEA (71.8%, p < 0.001) and cytology (85.9%, p = 0.210). AI may improve the diagnostic ability in differentiating malignant from benign pancreatic cystic lesions.

Highlights

There are different types of pancreatic cystic lesions, such as intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN), and pancreatic pseudocyst (PPC)
0.058b 0.002c 0.306c 0.406c 0.318c 0.008a
Diagnosis of malignant pancreatic cystic lesions is necessary for determining the appropriate treatment strategy

Summary

Introduction

There are different types of pancreatic cystic lesions, such as intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN), and pancreatic pseudocyst (PPC). Cyst fluid CEA level including other tumour markers is not useful in differentiating malignant from benign cystic lesions[12,14,15,16]. An artificial neural network with multiple hidden layers is called deep learning[21], which aims at learning multilevel representations of data to make predictions or classifications. This AI uses deep learning to analyse various images and extract clinical data using specific algorithm. This study aimed to investigate and compare the diagnostic ability of cyst fluid analysis of tumour markers and amylase, cytology, and AI combining pancreatic cyst fluid analysis and clinical data in differentiating malignant from benign pancreatic cystic lesions

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