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Endoscopy 2023; 55(02): 140-149
DOI: 10.1055/a-1873-7920
Original article

Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses

Takamichi Kuwahara 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Kazuo Hara 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Nobumasa Mizuno
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Shin Haba 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Nozomi Okuno 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Yasuhiro Kuraishi
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Daiki Fumihara
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Takafumi Yanaidani
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Sho Ishikawa
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Tsukasa Yasuda
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Masanori Yamada 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Sachiyo Onishi
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Keisaku Yamada
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Tsutomu Tanaka
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Masahiro Tajika 
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Yasumasa Niwa
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Rui Yamaguchi
3   Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Japan
4   Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Japan
,
Yasuhiro Shimizu
5   Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
› Author AffiliationsSupported by: Japan Society for the Promotion of Science JP 21K15938
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Abstract

Background There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognition of data patterns. This study aimed to investigate the efficacy of our AI model using endoscopic ultrasonography (EUS) images of multiple types of pancreatic mass (pancreatic ductal adenocarcinoma [PDAC], pancreatic adenosquamous carcinoma [PASC], acinar cell carcinoma [ACC], metastatic pancreatic tumor [MPT], neuroendocrine carcinoma [NEC], neuroendocrine tumor [NET], solid pseudopapillary neoplasm [SPN], chronic pancreatitis, and autoimmune pancreatitis [AIP]).

Methods Patients who underwent EUS were included in this retrospective study. The included patients were divided into training, validation, and test cohorts. Using these cohorts, an AI model that can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions was developed using a deep-learning architecture and the diagnostic performance of the AI model was evaluated.

Results 22 000 images were generated from 933 patients. The area under the curve, sensitivity, specificity, and accuracy (95 %CI) of the AI model for the diagnosis of pancreatic carcinomas in the test cohort were 0.90 (0.84–0.97), 0.94 (0.88–0.98), 0.82 (0.68–0.92), and 0.91 (0.85–0.95), respectively. The per-category sensitivities (95 %CI) of each disease were PDAC 0.96 (0.90–0.99), PASC 1.00 (0.05–1.00), ACC 1.00 (0.22–1.00), MPT 0.33 (0.01–0.91), NEC 1.00 (0.22–1.00), NET 0.93 (0.66–1.00), SPN 1.00 (0.22–1.00), chronic pancreatitis 0.78 (0.52–0.94), and AIP 0.73 (0.39–0.94).

Conclusions Our developed AI model can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions, but external validation is needed.

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Publication History

Received: 16 January 2022

Accepted after revision: 10 June 2022

Accepted Manuscript online:
10 June 2022

Article published online:
29 September 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 WHO Classification of Tumours Editorial Board. Digestive System Tumors. WHO classification of tumors. 5th edn.. Lyon: IARC Press; 2019
    Google Scholar
  • 2 Fusaroli P, Napoleon B, Gincul R. et al. The clinical impact of ultrasound contrast agents in EUS: a systematic review according to the levels of evidence. Gastrointest Endosc 2016; 84: 587-596.e10
    CrossrefPubMedGoogle Scholar
  • 3 Kuwahara T, Hara K, Mizuno N. et al. Present status of ultrasound elastography for the diagnosis of pancreatic tumors: review of the literature. J Med Ultrason 2020; 47: 413-420
    CrossrefPubMedGoogle Scholar
  • 4 Hirooka Y, Kuwahara T, Irisawa A. et al. JSUM ultrasound elastography practice guidelines: pancreas. J Med Ultrasonics 2015; 42: 151-174
    CrossrefPubMedGoogle Scholar
  • 5 Wiresema MJ, Vilmann P, Giovannini M. et al. Endosonography-guided fine-needle aspiration biopsy: diagnostic accuracy and complication assessment. Gastroenterology 1997; 112: 1087-1095
    CrossrefPubMedGoogle Scholar
  • 6 Haba S, Yamao K, Bhatia V. et al. Diagnostic ability and factors affecting accuracy of endoscopic ultrasound-guided fine needle aspiration for pancreatic solid lesions: Japanese large single center experience. J Gastroenterol 2013; 48: 973-981
    CrossrefPubMedGoogle Scholar
  • 7 Hewitt MJ, McPhail MJ, Possamai L. et al. EUS-guided FNA for diagnosis of solid pancreatic neoplasms: a meta-analysis. Gastrointest Endosc 2012; 75: 319-331
    CrossrefPubMedGoogle Scholar
  • 8 Kurita Y, Kuwahara T, Hara K. et al. Features of chronic pancreatitis by endoscopic ultrasound influence the diagnostic accuracy of endoscopic ultrasound-guided fine-needle aspiration of small pancreatic lesions. Dig Endosc 2020; 32: 399-408
    CrossrefPubMedGoogle Scholar
  • 9 Hu DM, Gong TT, Zhu Q. Endoscopic ultrasound elastography for differential diagnosis of pancreatic masses: a meta-analysis. Dig Dis Sci 2013; 58: 1125-1131
    CrossrefPubMedGoogle Scholar
  • 10 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-444
    CrossrefPubMedGoogle Scholar
  • 11 Kuwahara T, Hara K, Mizuno N. et al. Current status of artificial intelligence analysis for endoscopic ultrasonography. Dig Endosc 2021; 33: 298-305
    CrossrefPubMedGoogle Scholar
  • 12 Ting DSW, Cheung CY, Lim G. et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017; 318: 2211-2223
    CrossrefPubMedGoogle Scholar
  • 13 Gulshan V, Peng L, Coram M. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316: 2402-2410
    CrossrefPubMedGoogle Scholar
  • 14 Byrne MF, Chapados N, Soudan F. et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019; 68: 94-100
    CrossrefPubMedGoogle Scholar
  • 15 Hirasawa T, Aoyama K, Tanimoto T. et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21: 653-660
    CrossrefPubMedGoogle Scholar
  • 16 Horie Y, Yoshio T, Aoyama K. et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 2019; 89: 25-32
    CrossrefPubMedGoogle Scholar
  • 17 Hirai K, Kuwahara T, Furukawa K. et al. Artificial intelligence-based diagnosis of upper gastrointestinal subepithelial lesions on endoscopic ultrasonography images. Gastric Cancer 2022; 25: 382-391
    CrossrefPubMedGoogle Scholar
  • 18 Zhu M, Xu C, Yu J. et al. Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: A diagnostic test. PLoS One 2013; 8: e63820
    CrossrefPubMedGoogle Scholar
  • 19 Das A, Nguyen CC, Li F. et al. Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue. Gastrointest Endosc 2008; 67: 861-867
    CrossrefPubMedGoogle Scholar
  • 20 Ozkan M, Cakiroglu M, Kocaman O. et al. Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images. Endosc Ultrasound 2016; 5: 101-107
    CrossrefPubMedGoogle Scholar
  • 21 Săftoiu A, Vilmann P, Dietrich CF. et al. Quantitative contrast-enhanced harmonic EUS in differential diagnosis of focal pancreatic masses (with videos). Gastrointest Endosc 2015; 82: 59-69
    CrossrefPubMedGoogle Scholar
  • 22 Săftoiu A, Vilmann P, Gorunescu F. et al. Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer. Gastrointest Endosc 2008; 68: 1086-1094
    CrossrefPubMedGoogle Scholar
  • 23 Marya N, Powers P, Chari S. et al. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis. Gut 2021; 70: 1335-1344
    CrossrefPubMedGoogle Scholar
  • 24 Kuwahara T, Hara K, Mizuno N. et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas. Clin Transl Gastroenterol 2019; 10: 1-8
    Google Scholar
  • 25 Wang S, Liu W, Wu L. et al. Training deep neural networks on imbalanced data sets. 2016 International Joint Conference on Neural Networks (IJCNN) 2016; 4368-4374
    CrossrefPubMedGoogle Scholar
  • 26 Lloyd RV, Osamura R, Kloppel G, Rosai J. WHO Classification of Tumours of Endocrine Organs. 4th edn. Lyon: IARC Press; 2017
    Google Scholar
  • 27 Okazaki K, Chari ST, Frulloni L. et al. International consensus for the treatment of autoimmune pancreatitis. Pancreatology 2017; 17: 1-6
    CrossrefPubMedGoogle Scholar
  • 28 World Medical Association Inc. Declaration of Helsinki. Ethical principles for medical research involving human subjects. J Indian Med Assoc 2009; 107: 403-405
    PubMedGoogle Scholar
  • 29 Goodfellow IJ, Pouget-Abadie J, Mirza M. et al. Generative adversarial nets. Adv Neural Inf Process Syst 2014; 27: 2672-2680
    PubMedGoogle Scholar
  • 30 Tan M, Le QV. EffIicientNetV2: Smaller models and faster training. Proceedings of the 34th international conference on machine learning. PMLR 2021; 139: 10096-10106
    PubMedGoogle Scholar
  • 31 Yao Y, Rosasco L, Caponnetto A. On early stopping in gradient descent learning. Constr Approx 2007; 26: 289-315
    CrossrefPubMedGoogle Scholar
  • 32 Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data 2019; 6: 60
    CrossrefPubMedGoogle Scholar
  • 33 He K, Zhang X, Ren S. et al. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2016; 770-778
    PubMedGoogle Scholar
  • 34 Zhang J, Zhu L, Yao L. et al. Deep-learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video). Gastrointest Endosc 2020; 92: 874-885.e3
    CrossrefPubMedGoogle Scholar
  • 35 Tonozuka R, Itoi T, Nagata N. et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study. J Hepatobiliary Pancreat Sci 2021; 28: 95-104
    CrossrefPubMedGoogle Scholar