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Review
. 2023 Apr 22;15(9):2410.
doi: 10.3390/cancers15092410.

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Joanna Jiang  1 , Wei-Lun Chao  2 , Stacey Culp  3 , Somashekar G Krishna  4
Affiliations
Review

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Joanna Jiang et al. Cancers (Basel). .

Abstract

Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

Keywords: IPMN; artificial intelligence; endoscopy; pancreatic cysts; pancreatic ductal adenocarcinoma.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Precancerous lesions of the pancreas. Mucinous cysts: IPMNs and mucinous neoplasms (MCN). Non-mucinous cysts: serous pseudopapillary neoplasms (SPN), lymphoepithelial cysts (LEC), and cystic neuroendocrine tumors (NET). Pseudocysts, serous cystadenomas, and LEC are benign lesions.
Figure 2
Figure 2
AI developments in PDAC diagnosis and treatment. Machine learning has been leveraged to identify high-risk populations for PDAC screening, risk-stratify incidentally discovered PCLs, and assist with PDAC prognostication.
See this image and copyright information in PMC

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