Somashekar G. Krishna , Ahmed Abdelbaki , Ziwei Li , Stacey Culp , Xinqi Xiong , Bertrand Napoleon , Shaffer Mok , Helga Bertani , Yunlu Feng , Pradermchai Kongkam , Anjuli K. Luthra , Jorge D. Machicado , Samer El-Dika , Sarah Leblanc , Damien Meng Yew Tan , Jordan Burlen , Margaret G. Keane , Tara Keihanian , Antonio Mendoza Ladd , Thiruvengadam Muniraj , Wei-Lun Chao
{"title":"迈向导管内乳头状粘液瘤的自动化风险分层:人工智能在共聚焦激光内镜下超越了人类的专业知识。","authors":"Somashekar G. Krishna , Ahmed Abdelbaki , Ziwei Li , Stacey Culp , Xinqi Xiong , Bertrand Napoleon , Shaffer Mok , Helga Bertani , Yunlu Feng , Pradermchai Kongkam , Anjuli K. Luthra , Jorge D. Machicado , Samer El-Dika , Sarah Leblanc , Damien Meng Yew Tan , Jordan Burlen , Margaret G. Keane , Tara Keihanian , Antonio Mendoza Ladd , Thiruvengadam Muniraj , Wei-Lun Chao","doi":"10.1016/j.pan.2025.05.011","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and aims</h3><div><span><span>Endoscopic Ultrasound (EUS)-guided needle-based </span>confocal laser endomicroscopy<span> (nCLE) enables real-time microscopic visualization of pancreatic cyst epithelium and can identify high-grade dysplasia/invasive adenocarcinoma (HGD/IC) in branch-duct (BD) </span></span>intraductal papillary mucinous neoplasms<span> (IPMNs). We aimed to compare the performance of experts (humans) with artificial intelligence (AI) in stratifying dysplasia in BD-IPMNs.</span></div></div><div><h3>Methods</h3><div><span>This post-hoc analysis involved BD-IPMNs with definitive diagnoses from prospective EUS-nCLE studies (2015–2023) enrolled at a single center. Dysplasia grade was reviewed by two pathologists. Blinded EUS-nCLE experts reviewed unedited nCLE videos to classify dysplasia without and with revised Fukuoka criteria (revised-FC). The AI model, </span><em>nCLE-AI</em>, was similarly analyzed. Diagnostic parameters and AUC were compared to evaluate human and <em>nCLE-AI</em> performance.</div></div><div><h3>Results</h3><div>Among 60 BD-IPMNs (mean size = 3.43 ± 1.00 cm), 23 (38.3 %) had HGD/IC. To detect HGD/IC using nCLE, interobserver agreement (IOA) among 16 nCLE experts was ‘fair’ (κ = 0.29, 95 % CI: 0.27–0.32), with a sensitivity of 58 %, specificity of 59 %, and AUC of 0.59 (95 % CI 0.55–0.62). Incorporating revised-FC improved the sensitivity to 72 % and AUC to 0.64 (95 % CI 0.61–0.68; p < 0.001), with similar IOA (κ = 0.36 ‘fair’, 95 % CI: 0.33–0.38) and specificity (57 %).</div><div>Comparatively, <em>nCLE-AI</em> achieved 87 % sensitivity, 54 % specificity, and an AUC of 0.70 (0.57–0.84). When combined with revised-FC, <em>nCLE-AI</em> reached 78 % sensitivity, 78 % specificity, and an AUC of 0.85 (95 % CI: 0.74–0.96, p = 0.02), significantly higher than humans with revised-FC (p < 0.01).</div></div><div><h3>Conclusions</h3><div>Human dysplasia classification of BD-IPMNs using nCLE showed modest IOA and accuracy. In contrast, <em>nCLE-AI</em> classifications combined with clinical criteria offer superior accuracy for detecting HGD/IC while eliminating interobserver variability.</div></div>","PeriodicalId":19976,"journal":{"name":"Pancreatology","volume":"25 5","pages":"Pages 658-666"},"PeriodicalIF":2.7000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards automating risk stratification of intraductal papillary mucinous Neoplasms: Artificial intelligence advances beyond human expertise with confocal laser endomicroscopy\",\"authors\":\"Somashekar G. Krishna , Ahmed Abdelbaki , Ziwei Li , Stacey Culp , Xinqi Xiong , Bertrand Napoleon , Shaffer Mok , Helga Bertani , Yunlu Feng , Pradermchai Kongkam , Anjuli K. Luthra , Jorge D. Machicado , Samer El-Dika , Sarah Leblanc , Damien Meng Yew Tan , Jordan Burlen , Margaret G. Keane , Tara Keihanian , Antonio Mendoza Ladd , Thiruvengadam Muniraj , Wei-Lun Chao\",\"doi\":\"10.1016/j.pan.2025.05.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and aims</h3><div><span><span>Endoscopic Ultrasound (EUS)-guided needle-based </span>confocal laser endomicroscopy<span> (nCLE) enables real-time microscopic visualization of pancreatic cyst epithelium and can identify high-grade dysplasia/invasive adenocarcinoma (HGD/IC) in branch-duct (BD) </span></span>intraductal papillary mucinous neoplasms<span> (IPMNs). We aimed to compare the performance of experts (humans) with artificial intelligence (AI) in stratifying dysplasia in BD-IPMNs.</span></div></div><div><h3>Methods</h3><div><span>This post-hoc analysis involved BD-IPMNs with definitive diagnoses from prospective EUS-nCLE studies (2015–2023) enrolled at a single center. Dysplasia grade was reviewed by two pathologists. Blinded EUS-nCLE experts reviewed unedited nCLE videos to classify dysplasia without and with revised Fukuoka criteria (revised-FC). The AI model, </span><em>nCLE-AI</em>, was similarly analyzed. Diagnostic parameters and AUC were compared to evaluate human and <em>nCLE-AI</em> performance.</div></div><div><h3>Results</h3><div>Among 60 BD-IPMNs (mean size = 3.43 ± 1.00 cm), 23 (38.3 %) had HGD/IC. To detect HGD/IC using nCLE, interobserver agreement (IOA) among 16 nCLE experts was ‘fair’ (κ = 0.29, 95 % CI: 0.27–0.32), with a sensitivity of 58 %, specificity of 59 %, and AUC of 0.59 (95 % CI 0.55–0.62). Incorporating revised-FC improved the sensitivity to 72 % and AUC to 0.64 (95 % CI 0.61–0.68; p < 0.001), with similar IOA (κ = 0.36 ‘fair’, 95 % CI: 0.33–0.38) and specificity (57 %).</div><div>Comparatively, <em>nCLE-AI</em> achieved 87 % sensitivity, 54 % specificity, and an AUC of 0.70 (0.57–0.84). When combined with revised-FC, <em>nCLE-AI</em> reached 78 % sensitivity, 78 % specificity, and an AUC of 0.85 (95 % CI: 0.74–0.96, p = 0.02), significantly higher than humans with revised-FC (p < 0.01).</div></div><div><h3>Conclusions</h3><div>Human dysplasia classification of BD-IPMNs using nCLE showed modest IOA and accuracy. In contrast, <em>nCLE-AI</em> classifications combined with clinical criteria offer superior accuracy for detecting HGD/IC while eliminating interobserver variability.</div></div>\",\"PeriodicalId\":19976,\"journal\":{\"name\":\"Pancreatology\",\"volume\":\"25 5\",\"pages\":\"Pages 658-666\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pancreatology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1424390325001073\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pancreatology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1424390325001073","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
Towards automating risk stratification of intraductal papillary mucinous Neoplasms: Artificial intelligence advances beyond human expertise with confocal laser endomicroscopy
Background and aims
Endoscopic Ultrasound (EUS)-guided needle-based confocal laser endomicroscopy (nCLE) enables real-time microscopic visualization of pancreatic cyst epithelium and can identify high-grade dysplasia/invasive adenocarcinoma (HGD/IC) in branch-duct (BD) intraductal papillary mucinous neoplasms (IPMNs). We aimed to compare the performance of experts (humans) with artificial intelligence (AI) in stratifying dysplasia in BD-IPMNs.
Methods
This post-hoc analysis involved BD-IPMNs with definitive diagnoses from prospective EUS-nCLE studies (2015–2023) enrolled at a single center. Dysplasia grade was reviewed by two pathologists. Blinded EUS-nCLE experts reviewed unedited nCLE videos to classify dysplasia without and with revised Fukuoka criteria (revised-FC). The AI model, nCLE-AI, was similarly analyzed. Diagnostic parameters and AUC were compared to evaluate human and nCLE-AI performance.
Results
Among 60 BD-IPMNs (mean size = 3.43 ± 1.00 cm), 23 (38.3 %) had HGD/IC. To detect HGD/IC using nCLE, interobserver agreement (IOA) among 16 nCLE experts was ‘fair’ (κ = 0.29, 95 % CI: 0.27–0.32), with a sensitivity of 58 %, specificity of 59 %, and AUC of 0.59 (95 % CI 0.55–0.62). Incorporating revised-FC improved the sensitivity to 72 % and AUC to 0.64 (95 % CI 0.61–0.68; p < 0.001), with similar IOA (κ = 0.36 ‘fair’, 95 % CI: 0.33–0.38) and specificity (57 %).
Comparatively, nCLE-AI achieved 87 % sensitivity, 54 % specificity, and an AUC of 0.70 (0.57–0.84). When combined with revised-FC, nCLE-AI reached 78 % sensitivity, 78 % specificity, and an AUC of 0.85 (95 % CI: 0.74–0.96, p = 0.02), significantly higher than humans with revised-FC (p < 0.01).
Conclusions
Human dysplasia classification of BD-IPMNs using nCLE showed modest IOA and accuracy. In contrast, nCLE-AI classifications combined with clinical criteria offer superior accuracy for detecting HGD/IC while eliminating interobserver variability.
期刊介绍:
Pancreatology is the official journal of the International Association of Pancreatology (IAP), the European Pancreatic Club (EPC) and several national societies and study groups around the world. Dedicated to the understanding and treatment of exocrine as well as endocrine pancreatic disease, this multidisciplinary periodical publishes original basic, translational and clinical pancreatic research from a range of fields including gastroenterology, oncology, surgery, pharmacology, cellular and molecular biology as well as endocrinology, immunology and epidemiology. Readers can expect to gain new insights into pancreatic physiology and into the pathogenesis, diagnosis, therapeutic approaches and prognosis of pancreatic diseases. The journal features original articles, case reports, consensus guidelines and topical, cutting edge reviews, thus representing a source of valuable, novel information for clinical and basic researchers alike.