Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma.
IPMN
artificial intelligence
endoscopy
pancreatic cysts
pancreatic ductal adenocarcinoma
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
22 Apr 2023
22 Apr 2023
Historique:
received:
13
02
2023
revised:
20
04
2023
accepted:
20
04
2023
medline:
13
5
2023
pubmed:
13
5
2023
entrez:
13
5
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37173876
pii: cancers15092410
doi: 10.3390/cancers15092410
pmc: PMC10177524
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : NCI NIH HHS
ID : R01 CA279965
Pays : United States
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