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HUMAN-MACHINE INTERACTION IN OPTICAL POLYP DIAGNOSIS: DECISION-MAKING AFTER COMPUTER ASSISTED OPTICAL POLYP DIAGNOSIS

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HUMAN-MACHINE INTERACTION IN OPTICAL POLYP DIAGNOSIS: DECISION-MAKING AFTER COMPUTER ASSISTED OPTICAL POLYP DIAGNOSIS

Delphine Dubois 1, Yousr Jalal 1, Heiko Pohl 2, Douglas Kevin Rex 3, Cesare Hassan 4, Daniel von Renteln 5

Affiliations

1 University of Montreal, Montreal, Canada

2 White River Junction VA Medical Center, New Hampshire, United States

3 IUPUI, Indianapolis, United States

4 Humanitas University, Pieve Emanuele MI, Italy

5 CHUM, Montreal, Canada

Summary

AI Generated

Endoscopists correctly accept 89.1% of CADx neoplastic polyp classifications but frequently misclassify hyperplastic polyps as sessile serrated lesions, demonstrating a diagnostic bias toward neoplasia that may lead to unnecessary early surveillance colonoscopies.

Among 753 polyps from 367 patients, 89.1% (95% CI: 85.8–91.7) of accepted CADx neoplastic diagnoses were correct, while 68.7% (95% CI: 58.5–77.4) of accepted CADx hyperplastic diagnoses were accurate.
Endoscopists accepted incorrect diagnoses significantly more often when CADx predicted hyperplastic than neoplastic polyps (31.3% vs 10.9%).
When endoscopists rejected CADx adenoma classifications (N=47), 53.2% (95% CI: 38.2–67.6) were falsely reclassified as sessile serrated lesions.
Among rejected hyperplastic CADx predictions, 41.6% (95% CI: 33.7–50.0) were incorrectly reclassified as sessile serrated lesions.
The diagnostic bias toward neoplasia may be safe but could result in unnecessary early follow-up colonoscopies and increased costs.
CADx systems with robust SSL recognition capabilities are likely crucial for successful integration into routine clinical practice, as current systems use binary neoplastic/hyperplastic classification while endoscopists use three categories including SSL.

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This summary was generated by an AI large language model based on the content transcript. It is for informational purposes only and should not be considered a substitute for clinical judgment. Always rely on your professional expertise and the full clinical context when making clinical decisions.

Abstract

Introduction

Any computer-assisted characterization (CADx) system relies on Artificial Intelligence algorithms to provide a probability score to the endoscopist, characterizing the polyp as an adenoma or a hyperplastic polyp.^1-3However, the clinical utility of these AI-generated outputs is dependent on the endoscopist’s ability to interpret and integrate them into their final diagnostic decision-making process. This requires not only recognizing and accepting a true positive AI diagnosis, but also accurately identifying and rejecting false positives. This requires a higher level of endoscopist expertise to discern whether an AI-generated classification is accurate and should be incorporated into the final diagnosis or requires correction. The primary challenge in CADx implementation lies in this inherent complexity of human-AI interactions, which can lead to misinterpretation, resulting in the endoscopist either rejecting correct CADx outputs or accepting incorrect classifications, compromising diagnostic accuracy.

Aims & Methods

We conducted a secondary analysis from a prospective study. Endoscopists were first presented with the CADx classification for each polyp, which they could accept or reject and provide an alternative. A total of 1143 patients aged at least 45 underwent colonoscopy between June 2022 and January 2025 at the CHUM Hospital, yielding 753 polyps from 367 patients after exclusion criteria. The primary aim was to assess the agreement/rejection patterns of the CADx diagnosis by the endoscopists, stratified by categorization. Secondary outcomes included the rate at which clinicians correctly rejected inaccurate CADx outputs, as well as trends in post-rejection reclassification.

Results

A total of 89.1% (408/458, 95% CI: (85.8,91.7)) of accepted CADx neoplastic diagnoses were correct, and 68.7% (68/99, 95% CI: (58.5,77.4)) of accepted CADx hyperplastic diagnoses were accurate. However, endoscopists accepted incorrect diagnoses significantly more often when CADx predicted hyperplastic than neoplastic (%, 95% CI: 31.3% (22.6,41.5) vs 10.9% (8.3,14.2)). Regarding the post-rejection patterns, when endoscopists rejected adenoma classifications (N=47), they falsely classified 25 cases (53.2%, 95% CI: (38.2,67.6)) as a sessile serrated lesion/polyp (SSL/P). Similarly, although 43.6% of the rejected hyperplastic CADx predictions were correctly re-assigned an adenoma (31.5%, 95% CI: (24.3,39.7)) or SSL/P diagnosis (12.1%, 95% CI: (7.5,18.7)), there were 41.6% (95% CI: (33.7,50.0)) that were incorrectly re-classified as SSL/Ps.

Conclusion

Endoscopists generally accept accurate adenoma predictions provided by CADx systems but frequently misclassify hyperplastic polyps as SSLs, demonstrating a diagnostic bias toward neoplasia. Although beneficial from a safety perspective, this bias may result in unnecessary early follow-up colonoscopies, increasing associated costs.^4,5 Developing and integrating CADx systems with robust SSL recognition capabilities seems essential to overcome these diagnostic challenges and enhance clinical decision-making when using CADx assisted optical polyp diagnosis. Such advanced CADx systems are likely crucial for successful CADx integration into routine clinical practice.

References

1. Djinbachian R, Taghiakbari M, Barkun A, et al. Optimized computer-assisted technique for increasing adenoma detection during colonoscopy: a randomized controlled trial. Surg Endosc 2025;39:1120-1127.
2. Byrne MF, Von Renteln D, Barkun AN. Artificial Intelligence-Aided Colonoscopy for Characterizing and Detecting Colorectal Polyps: Required, Nice to Have, or Overhyped? Gastroenterology 2023;164:332-333.
3. Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021;27:8103-8122.à
4. Force AAT, Parasa S, Berzin T, et al. Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology. Gastrointest Endosc 2025;101:2-9 e1.
5. Halvorsen N, Barua I, Kudo SE, et al. Leaving colorectal polyps in situ with endocytoscopy assisted by computer-aided diagnosis: a cost-effectiveness study. Endoscopy 2025.

Disclosure

Daniel von Renteln has received research funding from ERBE Elektromedizin GmbH, Ventage, Pendopharm, Fujifilm and Pentax, and has received consultant or speaker fees from Boston Scientific Inc., ERBE Elektromedizin GmbH, and Pendopharm. Roupen Djinbachian has received speaker fees from Fujifilm. The remaining authors declare that they have no conflict of interest.

Event

UEG Week Berlin 2025

Topics

Endoscopy

Submission format

Abstract

Session

Detecting polyps with the help of AI

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

More Like This:

COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

Kenji Ikezawa

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Guilherme Macedo

DEVELOPMENT AND FUTURE PERSPECTIVES OF AN AI-BASED IMAGING SUPPORT SYSTEM FOR EARLY DETECTION OF PANCREATIC CANCER

Yuzo Kodama

A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

Manuel Limbert

TRANSFORMING RADIATION PROCTOPATHY MANAGEMENT: REAL-WORLD EVIDENCE ON PURASTAT FROM A UK TEACHING HOSPITAL

Vinoth Nadesalingam Vinoth Nadesalingam

A JAPANESE NATIONWIDE PROSPECTIVE OBSERVATIONAL STUDY ON EARLY PANCREATIC CANCER SCREENING IN HOSPITAL OUTPATIENTS USING A COMPREHENSIVE SCORING SYSTEM COMPARED WITH WESTERN SCREENING METHODS (EPSS-1)

Keiji Hanada

UEG Presentation

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COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

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COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

Itonaga Masahiro 1, Mamoru Takenaka 2, Hideyuki Shiomi 3, Koh Kitagawa 4, Shuhei Shintani 5, Hirotsugu Maruyama 6, Ryota Sagami 7, Tsukasa Ikeura 8, Takeshi Ogura 9, Yusuke Ishida 10, Koichiro Mandai 11, Satoshi Sugimori 12, Yoshiki Imamura 13, Atsuhiro Masuda 14, Kenji Ikezawa 15, Atsushi Shimizu 16, Misako Nagai 4, Atsushi Nakai 2, Ryota Nakano 3, Masayuki Kitano 16

Affiliations

1 Wakayama Medical University, Wakayama City, Japan

2 Kindai University Faculty of Medicine, Osaka, Japan

3 Hyogo Medical University, Hyogo, Japan

4 Nara Medical University, Nara, Japan

5 Shiga University of Medical Science, Otsu, Japan

6 Osaka City University Graduate School of Medicine, Osaka, Japan

7 Oita University, Yufu, Japan

8 Kansai Medical University, Hirakata, Japan

9 Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Japan

10 Fukuoka University Faculty of Medicine, Fukuoka, Japan

11 Kyoto Second Red Cross Hospital, Kyoto, Japan

12 Osaka City Genaral Hospital, Osaka, Japan

13 Ehime University Graduate School of Medicine, Ehime, Japan

14 Kobe graduate school of medicine, Kobe, Japan

15 Osaka International Cancer Institute, Osaka, Japan

16 Wakayama Medical University, Wakayama, Japan

Summary

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Abstract

Introduction

Preoperative biliary drainage (PBD) is commonly used in patients with malignant distal biliary obstruction (MDBO) to maintain performance status, prevent cholangitis and allow subsequent surgical or neoadjuvant treatment. Endoscopic retrograde cholangiopancreatography (ERCP) is the first-line approach, but in cases of failure, percutaneous transhepatic biliary drainage (PTBD) or endoscopic ultrasound-guided biliary drainage (EUS-BD) are considered. While EUS-BD has shown favourable outcomes compared to PTBD in the palliative setting, evidence comparing these modalities in the preoperative setting is limited.

Aims & Methods

The aim of this study was to compare the efficacy, safety, and surgical outcomes of PTBD and EUS-BD in patients with MDBO after failed ERCP. This retrospective multicenter study included patients who underwent PTBD or EUS-BD followed by pancreaticoduodenectomy (PD) for periampullary cancer at 15 referral centers in Japan between January 2012 and December 2021. Only patients who had failed or were not amenable to ERCP were included. The primary outcome was surgery-related adverse events (AEs), while secondary outcomes included drainage efficacy, recurrence patterns, disease-free survival (DFS), and overall survival (OS). Propensity score matching (PSM) was used to adjust for baseline differences.

Results

In total, 2350 patients received PBD before PD. Of the 79 patients in whom ERCP failed, 65 underwent PTBD and 14 underwent EUS-BD. After PSM, 13 matched pairs were analyzed. Technical and clinical success rates were comparable between groups. However, the EUS-BD group had a significantly higher internalization success rate (100% vs. 46%, P=0.005) and fewer drainage sessions required (median: 1 vs. 2, P=0.015). The EUS-BD group also had a significantly shorter hospital stay for drainage (11 vs. 17 days, P=0.042). Rates of drainage-related AEs tended to be lower in the EUS-BD group, although not statistically significant. Surgery-related outcomes, including operative time, intraoperative blood loss, R0 resection rate, postoperative hospital stay, and surgery-related AEs, were not significantly different between groups. No cases of needle tract seeding were observed. Median DFS and OS were comparable between groups (DFS: 14.2 vs. 16.8 months, P=0.22; OS: 17.9 vs. 27.9 months, P=0.167).

Conclusion

EUS-BD showed comparable surgical outcomes and long-term oncological efficacy to PTBD, while offering superior internalization success, fewer interventions, and a shorter preoperative hospital stay. These advantages make EUS-BD a favorable alternative to PTBD in the preoperative management of MDBO after failed ERCP, particularly in patients scheduled for neoadjuvant therapy.

Disclosure

Kitano M has received grants from Boston Scientific, Zeon Medical Inc., and Medicos Hirata Inc. Takenaka M, Shiomi H, Kitagawa K, Shintani S, Maruyama H, Sagami R, Ikeura T, Ogura T, Ishida Y, Mandai K, Sugimori S, Imamura Y, Masuda A, Ikezawa K,Shimizu A, Nakai A, Nagai M and Nakano R have no conflicts of interest or financial ties to disclose.

Event

UEG Week Berlin 2025

Topics

Endoscopy Hepatobiliary

Submission format

Abstract

Session

Endoscopic solutions to malignant biliary obstruction

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Kenji Ikezawa

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Guilherme Macedo

DEVELOPMENT AND FUTURE PERSPECTIVES OF AN AI-BASED IMAGING SUPPORT SYSTEM FOR EARLY DETECTION OF PANCREATIC CANCER

Yuzo Kodama

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Manuel Limbert

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Vinoth Nadesalingam Vinoth Nadesalingam

A JAPANESE NATIONWIDE PROSPECTIVE OBSERVATIONAL STUDY ON EARLY PANCREATIC CANCER SCREENING IN HOSPITAL OUTPATIENTS USING A COMPREHENSIVE SCORING SYSTEM COMPARED WITH WESTERN SCREENING METHODS (EPSS-1)

Keiji Hanada

UEG Presentation

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ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

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ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Miguel Mascarenhas-Saraiva 1, António Miguel Martins Pinto da Costa 2, Belén Agudo Castillo 2, Francisco Mendes 1, Jessica Widmer 3, Uzma D. Siddiqui 4, Tiago Ribeiro 1, Miguel Martins 1, Pedro Cardoso 1, Joana Mota 1, Maria Almeida 1, João Afonso 1, Grace E. Kim 4, Daniel De la Iglesia-García 2, Ana Pérez González 2, Carlos Esteban Fernández-Zarza 2, Ana Garcia Garcia de Paredes 5, Maria Moris 6, Matheus Ferreira de Carvalho 7, Marcos Eduardo Lera dos Santos 8, João Ferreira 9, Khoon Sheng Kok 10, Tamas Gonda 11, Joan B. Gornals 12, Mason Adams 13, Jack Di Palma 13, André Martins Dos Santos 14, Filipe Vilas-Boas 1, Pedro Moutinho-Ribeiro 1, Susana Lopes 1, Mariano Ganzalez- Haba Ruiz 2, Eduardo Guimarães Hourneaux de Moura 7, Guilherme Macedo 1

Affiliations

1 Centro Hospitalar Universitário de São João, Porto, Portugal

2 Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain

3 NYU Langone Health, New York, United States

4 Center for Endoscopic Research and Therapeutics (CERT) and Advanced Endoscopy Training, University of Chicago, Chicago, United States

5 Hospital Universitario Ramon y Cajal, Madrid, Spain

6 Hospital Universitario Marqués de Valdecilla, Santander, Spain

7 Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Brazil

8 Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Brazil|||Hospital São Luiz Jabaquara, São Paulo, Brazil

9 Faculty of Engineering of the University of Porto, Porto, Portugal

10 Royal Liverpool Hospital, Liverpool, United Kingdom

11 Columbia University, New York, United States

12 Hospital Universitari De Bellvitge, Bellvitge Biomedical Research Institute (idibell), Barcelona, Spain

13 University of South Alabama, Mobile, United States

14 Faculdade de Medicina da Universidade do Porto, Porto, Portugal

Summary

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Abstract

Introduction

Endoscopic ultrasound (EUS) is the gold standard for evaluating pancreatic lesions, allowing for real-time imaging and biopsy sampling. Nevertheless, the differentiation between solid pancreatic lesions — such as pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (PNETs) — as well as cystic lesions is often troublesome. Specifically, identifying intraductal papillary mucinous neoplasms (IPMNs) with varying degrees of dysplasia is crucial for guiding clinical management. However, the diagnostic accuracy of current methods remains suboptimal. This multicenter study aimed to develop and validate convolutional neural networks (CNNs) to characterize solid and cystic pancreatic lesions and stratify IPMNs into high-grade dysplasia/carcinoma (HGD/C) and low-grade dysplasia (LGD).

Aims & Methods

This multicenter study included EUS images from 12 centers in Spain, Brazil, the United Kingdom and the United States of America. Solid lesion differentiation (PDAC vs. PNET) was achieved using a CNN trained on 49034 images from 95 patients (n=69 for PDAC and n=26 for PNET), with diagnoses confirmed via EUS-guided fine-needle biopsy or surgical specimen analysis. A second CNN was trained on 79395 images from 82 patients (n=64 for mucinous cystic lesions and n=18 for non-mucinous cystic lesions). Finally, a third model used the subset of IPMNs (n=51046 frames, 30 patients) to distinguish between HGD/C (n=5 patients) and LGD (n=25 patients). Histological confirmation was used as the gold standard. Performance metrics included sensitivity, specificity, accuracy, and area under the precision-recall curve (AUC-PR). To comprehensively determine the model's performance, we evaluated multiple thresholds for the confidence score and the IoU, between 0.45 and 0.55 with a step of 0.02 ([0.45, 0.47, 0.50, 0.52, 0.55]). The datasets were split into training (70%), validation (20%), and testing (10%) subsets.

Results

For solid lesions, the model differentiated PDAC from PNETs with an accuracy of 91.8%, with sensitivities of 96.2% and 97.8%, respectively. The AUC-PR was 0.982 for PDAC and 0.977 for PNET. Regarding cystic lesions the model detected mucinous cystic lesions with a sensitivity of 97.5%, an overall accuracy of 89.7% and an AUC-PR of 0.977. When the subset of IPMNs was specifically analyzed, the model distinguished IPMNs with HGD/C from those with LGD with a sensitivity of 97.5%, specificity of 94.3%, and an overall accuracy of 89.8%. The AUC was 0.988.

Conclusion

This is the first multicenter, transatlantic AI study aiming to provide a holistic approach to both solid and cystic pancreatic lesions, focusing on those with a significantly higher malignant potential. Our models achieved high accuracy in identifying and differentiating these conditions. Furthermore, to our knowledge this is the first integrative model to include the ability to stratify IPMNs into LGD and HGD/C, which has significant clinical implications, potentially reducing unnecessary surgeries and ensuring timely intervention for high-risk patients. The inclusion of data from diverse regions reduces demographic bias and enhances model generalizability. Real-time implementation of these solutions could significantly improve decision-making in EUS and optimize patient outcomes.

Event

UEG Week Berlin 2025

Topics

Digestive Oncology Pancreas

Submission format

Abstract

Session

Prediction and prognosis of pre-malignant and malignant pancreatic lesions

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Kenji Ikezawa

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Guilherme Macedo

DEVELOPMENT AND FUTURE PERSPECTIVES OF AN AI-BASED IMAGING SUPPORT SYSTEM FOR EARLY DETECTION OF PANCREATIC CANCER

Yuzo Kodama

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Manuel Limbert

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Vinoth Nadesalingam Vinoth Nadesalingam

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Keiji Hanada

UEG Presentation

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DEVELOPMENT AND FUTURE PERSPECTIVES OF AN AI-BASED IMAGING SUPPORT SYSTEM FOR EARLY DETECTION OF PANCREATIC CANCER

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DEVELOPMENT AND FUTURE PERSPECTIVES OF AN AI-BASED IMAGING SUPPORT SYSTEM FOR EARLY DETECTION OF PANCREATIC CANCER

Atsuhiro Masuda 1, Keitaro Sofue 1, Takeru Yamaguchi 1, Nobuyuki Hirahara 2, Aya Ogasawara 2, Masanori Gonda 1, Mika Miki 1, Tetsuhisa Ko 1, Takashi Kobayashi 1, Arata Sakai 1, Norimitsu Uza 1, Mizuki Takei 2, Takamichi Murakami 1, Yuzo Kodama 1

Affiliations

1 Kobe Graduate School of Medicine, Kobe, Japan

2 Medical Systems Research and Development Center, FUJIFILM Corporation, Tokyo, Japan

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Abstract

Introduction

Early diagnosis of pancreatic cancer remains difficult due to the lack of overt findings on imaging. Our prior analysis revealed that indirect signs such as pancreatic atrophy and ductal stenosis or dilation often precede tumor visibility. In collaboration with Fujifilm Corporation, we developed an AI-based system capable of detecting pancreatic masses, atrophy, and ductal abnormalities on contrast-enhanced CT(CE-CT) and non-contrast CT(NC-CT). This study evaluated its diagnostic performance.

Aims & Methods

The AI algorithm extracts the pancreatic parenchyma, ducts, and tumor regions from CT images and classifies the presence of four imaging findings (pancreatic mass, atrophy, ductal dilation, and ductal stenosis). The model was trained and validated using approximately 2,000 images. For testing, we used an external dataset comprising 90 pancreatic cancer cases (Tis/T1: T2: T3–T4 = 1:1:1) and 100 controls collected from affiliated institutions. Reference standard for each imaging findings was established by two expert radiologists. Diagnosis of pancreas cancer was also performed using the logistic regression model with the results of each imaging finding. Diagnostic performances were performed on CE-CT and NC-CT, and AI diagnostic accuracy and reading time were compared with those of four radiologists and two gastroenterologists.

Results

For the diagnosis of pancreas cancer, the AI showed a sensitivity of 98.9% and specificity of 93.0% on CE-CT, and sensitivity of 83.9% and specificity of 92.0% on NC-CT. The system successfully detected early-stage cancers, including Tis and T1, by identifying indirect imaging findings. Detection metrics for CE-CT vs. NC-CT were as follows: mass (sensitivity 97.2% vs. 87.5%; specificity 74.6% vs. 71.4%), atrophy (74.0% vs. 59.6%; 90.7% vs. 92.9%), ductal dilation (78.8% vs. 90.5%; 96.4% vs. 88.3%), and ductal stenosis (87.7% vs. 93.9%; 90.4% vs. 85.1%).
Physicians showed mean sensitivities/specificities of 88.9%/99.3% on CE-CT and 53.4%/99.5% on NC-CT. Mean reading time per case was 22 seconds (AI) vs. 63 seconds (physicians) for CE-CT and 20 seconds (AI) vs. 78 seconds (physicians) for NC-CT.

Conclusion

The AI system demonstrated diagnostic performance comparable to that of experienced radiologists and significantly reduced reading time. While its specificity was somewhat lower than that of physicians, the system effectively identified early-stage pancreatic cancers using secondary findings. At present, it may be best applied as a second-reader support tool in radiological interpretation. Further refinement may expand its role in early detection and possibly future screening.

Disclosure

N.H., A.O., and M.T. are employees of Fujifilm Corporation.
A.M., K.S., T.M., and Y.K. declare financial conflicts of interest with Fujifilm Corporation.

Event

UEG Week Berlin 2025

Topics

Digestive Oncology Pancreas

Submission format

Abstract

Session

Prediction and prognosis of pre-malignant and malignant pancreatic lesions

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

More Like This:

COMPARISON OF ENDOSCOPIC ULTRASOUND-GUIDED VERSUS PERCUTANEOUS BILIARY DRAINAGE FOR PREOPERATIVE MANAGEMENT OF MALIGNANT DISTAL BILIARY OBSTRUCTION AFTER FAILED ERCP: A MULTICENTER RETROSPECTIVE STUDY

Kenji Ikezawa

ARTIFICIAL INTELLIGENCE FOR COMPREHENSIVE PANCREATIC LESION PROFILING: MULTICENTER VALIDATION OF SOLID/CYSTIC LESION DETECTION/CHARACTERIZATION AND IPMN GRADING

Guilherme Macedo

DEVELOPMENT AND FUTURE PERSPECTIVES OF AN AI-BASED IMAGING SUPPORT SYSTEM FOR EARLY DETECTION OF PANCREATIC CANCER

Yuzo Kodama

A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

Manuel Limbert

TRANSFORMING RADIATION PROCTOPATHY MANAGEMENT: REAL-WORLD EVIDENCE ON PURASTAT FROM A UK TEACHING HOSPITAL

Vinoth Nadesalingam Vinoth Nadesalingam

A JAPANESE NATIONWIDE PROSPECTIVE OBSERVATIONAL STUDY ON EARLY PANCREATIC CANCER SCREENING IN HOSPITAL OUTPATIENTS USING A COMPREHENSIVE SCORING SYSTEM COMPARED WITH WESTERN SCREENING METHODS (EPSS-1)

Keiji Hanada

UEG Presentation

Share via Email Share on Facebook Share on X Share on LinkedIn Share on Bluesky

A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

This content is part of Gutflix. Log in with your myUEG account, or create one free, to watch it.

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A NOVEL DEEP LEARNING MODEL FOR PREDICTING COLORECTAL ANASTOMOTIC DEHISCENCE: A PIONEER MULTICENTER TRANSATLANTIC STUDY

Miguel Mascarenhas-Saraiva 1, Francisco Mendes 1, André Martins dos Santos 2, António Miguel Martins Pinto da Costa 3, Filipa Fonseca 4, Eduardo Carvalho 5, Nilza Ramião 5, Guilherme Barbosa 5, Maisa Vasconcellos 6, Abdullah Bunaiyan 7, Maria Almeida 1, Joana Mota 1, Tiago Ribeiro 1, Miguel Martins 1, João Afonso 1, Pedro Cardoso 1, Francisca Pinheiro 2, Matheus Carvalho 3, Marcos Eduardo Lera dos Santos 3, Eduardo De Moura 3, Marley Feitosa 6, Ahsan Javed 7, Guilherme Macedo 1, Manuel Limbert 4

Affiliations

1 Centro Hospitalar Universitário de São João, Porto, Portugal

2 Faculdade de Medicina da Universidade do Porto, Porto, Portugal

3 Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Brazil

4 Instituto Português de Oncologia IPO, Lisboa, Portugal

5 INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal

6 Hospital das Clínicas da Faculdade de Medicina de USP - Ribeirão Preto, São Paulo, Brazil

7 Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom

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Abstract

Introduction

Colorectal anastomotic dehiscence (CAD) is one of the most severe postoperative complications in colorectal surgery, significantly impacting patient morbidity and mortality. Current risk assessment methods rely on clinical and intraoperative factors, but no real-time predictive tool exists. This study aimed to develop an artificial intelligence model based on intraoperative recording of the anastomosis for CAD prediction.

Aims & Methods

A convolutional neural network (CNN) was trained using a dataset of annotated frames from colorectal surgery videos across three international high-volume centers: IPO Lisboa, Hospital das Clínicas de Ribeirão Preto, São Paulo, Brazil and Royal Liverpool University Hospital. The dataset included a total of 5,356 annotated frames from 25 patients, 2,007 frames classified as CAD-positive and 3,349 as CAD-negative. Four CNN architectures (EfficientNetB0, EfficientNetB7, ResNet50, and MobileNetV2) were tested. Performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUROC) curve. Heatmaps were generated to identify key regions influencing predictions.

Results

The best-performing model achieved an accuracy of 99.55%, AUROC of 99.60%, sensitivity of 99.21%, specificity of 100%, PPV of 100%, and NPV of 98.94%. The model reliably identified CAD-positive frames and provided visual explanations through heatmaps.

Conclusion

To our knowledge, this is the first AI model developed to predict CAD using intraoperative video analysis. Its accuracy suggests the potential to redefine surgical decision-making by providing real-time risk assessment. Further refinement with a larger dataset and diverse surgical techniques could enable intraoperative interventions to prevent CAD before it occurs, marking a paradigm shift in colorectal surgery.

Event

UEG Week Berlin 2025

Topics

Colorectal Endoscopy Surgery

Submission format

Abstract

Session

Lower GI: Surgical considerations

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Keiji Hanada

UEG Presentation

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TRANSFORMING RADIATION PROCTOPATHY MANAGEMENT: REAL-WORLD EVIDENCE ON PURASTAT FROM A UK TEACHING HOSPITAL

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TRANSFORMING RADIATION PROCTOPATHY MANAGEMENT: REAL-WORLD EVIDENCE ON PURASTAT FROM A UK TEACHING HOSPITAL

MUHAMMAD HUSNAIN KHIZAR 1, Sergio Coda 1, Vinoth Nadesalingam Vinoth Nadesalingam 1

Affiliations

1 Barking, Havering And Redbridge University Hospitals NHS Trust, London, United Kingdom

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Abstract

Introduction

Radiation proctopathy (RP) is a common late complication of pelvic radiotherapy, affecting up to 20% of patients treated for pelvic malignancies. It manifests as rectal bleeding, anaemia, urgency, and diarrhoea, significantly reducing quality of life. Argon Plasma Coagulation (APC) remains the standard endoscopic therapy but has notable limitations, including high recurrence rates, the need for multiple sessions, and risks such as ulceration and perforation.
PuraStat, a synthetic self-assembling peptide hydrogel, offers an innovative alternative by creating a transparent matrix that promotes haemostasis and mucosal healing without thermal injury. While small series have shown benefit, robust real-world evidence is lacking.

Aims & Methods

We assessed real-world outcomes of PuraStat in RP at a UK tertiary centre (2023–2024). Consecutive patients with chronic radiation-induced rectal bleeding were included and treated by a single endoscopist. Patients were stratified into two cohorts:
1. De novo PuraStat – no prior APC (n=50)
2. APC→PuraStat – persistent or recurrent symptoms despite APC (n=50)
PuraStat was applied during flexible sigmoidoscopy, either as monotherapy or following APC. The primary outcome was resolution or improvement of rectal bleeding. Secondary outcomes included the number of repeat endoscopic interventions within 6 months and treatment-related adverse events.

Results

A total of 100 patients were included (median age: 74 years, range 64–85; 52% male). Most had prostate or gynaecological cancer treated with pelvic radiotherapy.
- De novo PuraStat (n=50): All achieved symptomatic relief, with marked bleeding reduction and endoscopic improvement. Follow-up sigmoidoscopy showed mucosal healing or mild residual proctitis in 92%. Mean repeat sessions: 1.3 (range 0–3) over 6 months.
- APC→PuraStat (n=50): These patients had undergone a median of 3 APC sessions with limited success. PuraStat led to complete symptom control in all cases, reducing the need for frequent interventions (mean: 1.5 sessions over 6 months).
No adverse events (e.g., perforation, infection, severe pain) were reported in either group. Patients with severe radiation-associated vascular ectasia (RAVE) and refractory bleeding demonstrated excellent response to PuraStat.

Conclusion

This large real-world series demonstrates that PuraStat is a safe, effective, and well-tolerated therapy for radiation proctopathy, whether used as first-line treatment or following APC failure. It provided rapid and sustained bleeding control, significantly reduced the need for repeat endoscopic interventions, and promoted mucosal healing without the risks associated with thermal therapies. Importantly, PuraStat showed consistent efficacy even in patients with severe radiation-associated vascular ectasia (RAVE) and those refractory to conventional modalities, highlighting its role as both a frontline and salvage option. These findings position PuraStat as a valuable addition to the therapeutic armamentarium for radiation-induced rectal bleeding. Larger prospective, multicentre studies and cost-effectiveness analyses are warranted to validate these results and define its optimal place in treatment algorithms.

Table 1: Treatment Outcomes by Group

Group	n	Symptom Relief	Mean Repeat Sessions
De novo PuraStat	50	100%	1.3
APC→PuraStat	50	100%	1.5

References

1- White K, Henson CC. Endoscopically delivered PuraStat for the treatment of severe haemorrhagic radiation proctopathy: Service evaluation of a new endoscopic treatment for a challenging condition. Frontline Gastroenterol. 2021;12(1):37–42. doi:10.1136/flgastro-2020-101735
2-Van de Wetering FT, Verleye L, Andreyev HJN, Maher J, Vlayen J, Pieters BR, et al. Non-surgical interventions for late rectal problems (proctopathy) of radiotherapy in people who have received radiotherapy to the pelvis. Cochrane Database Syst Rev. 2016;(4):CD003455. doi:10.1002/14651858.CD003455.pub2
3-Andreyev HJN, Davidson SE, Gillespie C, Allum WH, Swarbrick E. Practice guidance on the management of acute and chronic gastrointestinal problems arising as result of treatment for cancer. Gut. 2012;61:179–192. doi:10.1136/gutjnl-2011-300563
4-Dahiya DS, Kichloo A, Tuma F, Albosta M, Wani F. Radiation proctitis and management strategies. Clin Endosc. 2022;55(1):22–32. doi:10.5946/ce.2020.288
5-Rustagi T, Mashimo H. Endoscopic management of chronic radiation proctopathy. World J Gastroenterol. 2011;17(41):4554–4562. doi:10.3748/wjg.v17.i41.4554
6-Lenz L, Rohr R, Nakao F, Libera E, Ferrari A. Chronic radiation proctopathy: A practical review of endoscopic treatment. World J Gastrointest Surg. 2016;8(2):151–158. doi:10.4240/wjgs.v8.i2.151

Event

UEG Week Berlin 2025

Topics

Colorectal Digestive Oncology Endoscopy Histopathology

Submission format

Late-Breaking Abstract

Session

Colorectal ESD: The better way?

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Kenji Ikezawa

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Manuel Limbert

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Vinoth Nadesalingam Vinoth Nadesalingam

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Keiji Hanada

UEG Presentation

Share via Email Share on Facebook Share on X Share on LinkedIn Share on Bluesky

A JAPANESE NATIONWIDE PROSPECTIVE OBSERVATIONAL STUDY ON EARLY PANCREATIC CANCER SCREENING IN HOSPITAL OUTPATIENTS USING A COMPREHENSIVE SCORING SYSTEM COMPARED WITH WESTERN SCREENING METHODS (EPSS-1)

This content is part of Gutflix. Log in with your myUEG account, or create one free, to watch it.

Not sure what you can access? Learn more about account types.

A JAPANESE NATIONWIDE PROSPECTIVE OBSERVATIONAL STUDY ON EARLY PANCREATIC CANCER SCREENING IN HOSPITAL OUTPATIENTS USING A COMPREHENSIVE SCORING SYSTEM COMPARED WITH WESTERN SCREENING METHODS (EPSS-1)

Ryota Sagami 1, Kentaro Yamao 2, Akinori Shimizu 3, Hidefumi Nishikiori 4, Koji Yoshida 5, Mamoru Takenaka 6, Akihiro Yoshida 6, HAJIME IMAI 7, Katsunori Sekine 8, Masatoshi Murakami 9, Nao Fujimori 9, Rintaro Nagayama 10, Toshiharu Ueki 10, Jun Nakahodo 11, Kazuhisa Okamoto 12, Makiko Kinoshita 13, Yukiko Takayama 14, Yuji Amano 15, Kazuhiro Mizukami 12, Kazunari Murakami 16, Keiji Hanada 3

Affiliations

1 Faculty of Medicine, Oita University, Yufu, Japan|||Faculty of Medicine, Oita University, Yufu, Japan

2 Nagoya University, Nagoya, Japan

3 Onomichi General Hospital, Onomichi, Japan

4 Oita San-ai Medical Center, Oita, Japan

5 Faculty of Medicine, Kawasaki Medical University, Kurashiki, Japan

6 Kinki University, Osaka sayama, Japan

7 Okanami General Hospital, Iga, Japan

8 National Center for Global Health and Medicine, Kohnodai Hospital, Chiba, Japan

9 Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

10 Fukuoka University Chikushi Hospital, Fukuoka, Japan

11 Department of Gastroenterology, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome H, Tokyo, Japan

12 Faculty of Medicine, Oita University, Yufu, Japan

13 National Organization Minamiwakayama Medical Center, Tanabe, Japan

14 Tokyo Women's Medical University, Shinjuku, Japan

15 Urawa GIE clinic group, Urawa, Japan

16 Oita University Dept. of Gastroenterology, Yuhu, Japan

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Abstract

Introduction

Pancreatic cancer (PC) has one of the poorest prognoses among malignancies. Early detection, particularly of stage 0 or stage 1 disease, is crucial to improving survival outcomes. However, conventional screening strategies based solely on family history or symptoms—as commonly practiced in Western countries—have shown limited sensitivity for early-stage PC. In Japan, more comprehensive approaches are being developed.

Aims & Methods

Low-grade relevant (LGR) factor	High-grade relevant (HGR) factor
・sporadic family history ・diabetes mellitus ・diabetes mellitus worsening ・stomach/back symptoms ・weight loss ・elevated serum pancreatic enzyme ・heavy alcohol consumption ・smoking history ・obesity	・familial PC ・hereditary PC syndrome/pancreatitis ・new-onset diabetes mellitus ・jaundice ・elevated serum tumor biomarkers ・chronic pancreatitis ・pancreatic cyst ・main pancreatic duct dilation
Score positive; 3 points or more	Score positive; 1 point or more

This nationwide, multicenter prospective study aimed to evaluate the utility of a novel scoring system for early PC detection among hospital outpatients. From August 2019 to May 2024, all outpatients presenting with symptoms, risk factors, subtle imaging abnormalities of the pancreas, or requesting pancreatobiliary screening at the Department of Gastroenterology in hospitals participating in the Early Pancreatic Cancer Screening Study (EPSS) were considered for inclusion. Consecutive patients who had no pancreatic tumors suspected by any imaging modalities were examined, no prior history of endoscopic ultrasound (EUS) examination for pancreatic disease, and/or no referrals from other hospitals specifically for PC diagnosis were prospectively enrolled. The system comprised low- and high-grade relevant (LGR/HGR) factors referring to both risk factors and diagnostic indicators, including symptoms and subtle imaging abnormalities associated with PC. Patients were classified as score-positive if they had ≥3 LGR or ≥1 HGR factor(s). Diagnostic yield for stage 0/1 and all-stage PC was compared between score-positive and score-negative groups. Diagnostic performance was also evaluated against previous large-scale Japanese data and Western screening methods based on family/genetic risk or symptoms. The sample size was determined based on a previous large-scale PC screening study in Japan.

Results

Of 3,287 patients, 2,531 were score-positive and 756 were score-negative. PC was diagnosed in 283 patients (8.6%), including 67 with stage 0/1 disease. The detection rate of stage 0/1 PC was significantly higher in score-positive than in score-negative patients (2.6% vs. 0.7%, P=.001). Compared to a previous Japanese study (0.78%), this study showed a significantly higher detection rate (P<.001). The sensitivity of the scoring system for stage 0/1 PC was 93.1%, significantly outperforming Western-style approaches (12.5% for family/genetic risk and 50.0% for symptom-based screening; both P<.001), while maintaining comparable accuracy. Even combining family/genetic risk and symptoms, sensitivity remained lower than that of the scoring system (56.9%, P<.001). For all-stage PC, both the accuracy and sensitivity of the diagnosis based on the scoring system were significantly higher than those based on family/genetic risk, symptom, or the combination of symptom and family/genetic risk (P = .030, .004, .002 for accuracy; and all P <.001 for sensitivity, respectively). Among the 283 PC cases, 23.7% were stage 0/1, 15.9% were ≤10 mm, and 45.6% underwent surgical resection. Multivariate logistic analysis identified older age, worsening diabetes, and MPD dilation as significant independent predictors of stage 0/1 PC.

Conclusion

The scoring system demonstrated superior sensitivity and comparable accuracy to traditional screening strategies for detecting early-stage PC. Its use in outpatient settings may facilitate earlier diagnosis and intervention, thereby improving resectability and prognosis.

References

Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol 2021;18:493-502.
Hanada K, Okazaki A, Hirano N, et al. Diagnostic strategies for early pancreatic cancer. J Gastroenterol 2015;50:147-54.
Sagami R, Hayasaka K, Ujihara T, et al. Role of EUS combined with a newly modified scoring system to detect pancreatic high-grade precancerous lesions. Endosc Ultrasound 2022.

Event

UEG Week Berlin 2025

Topics

Digestive Oncology Pancreas

Submission format

Abstract

Session

Prediction and prognosis of pre-malignant and malignant pancreatic lesions

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Kenji Ikezawa

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Keiji Hanada