Özden Y, Yüzügülen Ö, Omurca F. Interpretable multimodal artificial intelligence model for predicting advanced neoplasia in pancreatic cystic lesions. World J Gastrointest Oncol 2026; 18(7): 119847 [DOI: 10.4251/wjgo.v18.i7.119847]
Corresponding Author of This Article
Yavuz Özden, MD, Department of Gastroenterology, Kayseri City Hospital, University of Health Sciences, Şeker District, Muhsin Yazıcıoğlu Boulevard No. 77 Kocasinan, Kayseri 38080, Türkiye. yavuzozden@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
research-article
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Özden Y, Yüzügülen Ö, Omurca F. Interpretable multimodal artificial intelligence model for predicting advanced neoplasia in pancreatic cystic lesions. World J Gastrointest Oncol 2026; 18(7): 119847 [DOI: 10.4251/wjgo.v18.i7.119847]
World J Gastrointest Oncol. Jul 15, 2026; 18(7): 119847 Published online Jul 15, 2026. doi: 10.4251/wjgo.v18.i7.119847
Interpretable multimodal artificial intelligence model for predicting advanced neoplasia in pancreatic cystic lesions
Yavuz Özden, Ömer Yüzügülen, Ferhat Omurca
Yavuz Özden, Ömer Yüzügülen, Ferhat Omurca, Department of Gastroenterology, Kayseri City Hospital, University of Health Sciences, Kayseri 38080, Türkiye
Author contributions: Özden Y designed the study; Özden Y and Yüzügülen Ö developed the methodology; Omurca F acquired the data; Özden Y performed the statistical analysis and drafted the manuscript; all authors revised the manuscript and approved the final version.
AI contribution statement: AI-assisted language tools were used in a limited manner for language polishing and editorial refinement during manuscript revision, including Paperpal.
Institutional review board statement: The study was approved by the Ethics Committee of the University of Health Sciences, Kayseri City Hospital (Protocol No. 602; approval date: October 13, 2025).
Informed consent statement: The requirement for informed consent was waived by the Ethics Committee due to the retrospective design and use of anonymized data.
Conflict-of-interest statement: The authors declare no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: De-identified data are available from the corresponding author upon reasonable request.
Corresponding author: Yavuz Özden, MD, Department of Gastroenterology, Kayseri City Hospital, University of Health Sciences, Şeker District, Muhsin Yazıcıoğlu Boulevard No. 77 Kocasinan, Kayseri 38080, Türkiye. yavuzozden@gmail.com
Received: February 7, 2026 Revised: February 28, 2026 Accepted: April 20, 2026 Published online: July 15, 2026 Processing time: 149 Days and 9.6 Hours
Abstract
BACKGROUND
International guidelines for pancreatic cystic lesions (PCLs) rely primarily on morphology-based criteria and demonstrate limited discrimination for advanced neoplasia. The integration of clinical, endoscopic ultrasound (EUS), and cyst fluid biomarkers into interpretable risk models may improve preoperative risk stratification.
AIM
To develop and internally validate an interpretable multimodal model for predicting advanced neoplasia in PCLs.
METHODS
This retrospective single-center cohort study included 187 adults who underwent surgical resection for PCLs between 2012 and 2025. Clinical variables, cross-sectional imaging findings, EUS features, and cyst fluid biomarkers (carcinoembryonic antigen and glucose) were analyzed. Using least absolute shrinkage and selection operator-penalized logistic regression, a core clinical-EUS model and an integrated multimodal model were developed (training set, n = 131) and internally validated (n = 56). Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Calibration and decision curve analysis were also performed. Model performance was compared with American Gastroenterological Association 2015, Fukuoka 2017, European 2018, and Kyoto 2024 criteria.
RESULTS
Advanced neoplasia was identified in 58 of 187 patients (31.0%). In the validation cohort (17 advanced; 39 non-advanced), the integrated multimodal model achieved an AUC of 0.91 (95%CI: 0.82-0.97), with a sensitivity of 82.4% and specificity of 89.7%, significantly outperforming international guideline-based criteria (AUC range: 0.70-0.79; all P < 0.01). Performance remained stable in the intraductal papillary mucinous neoplasm subset (AUC 0.90) and in cysts without mural nodules (AUC 0.88). Decision curve analysis demonstrated a superior net benefit across clinically relevant thresholds.
CONCLUSION
An interpretable multimodal model integrating clinical, EUS, and cyst fluid data improves the discrimination of advanced neoplasia in surgically resected PCLs and may support preoperative risk stratification. Prospective external validation is required before clinical implementation.
Core Tip: Current guideline-based algorithms for pancreatic cystic lesions predominantly rely on morphology and show limited discrimination for advanced neoplasia. In this retrospective cohort study of 187 surgically resected lesions, we developed and internally validated an interpretable multimodal model integrating clinical variables, endoscopic ultrasound features, and cyst fluid biomarkers. The model achieved superior discrimination compared with international guideline criteria and demonstrated stable performance across key subgroups. This interpretable framework may support preoperative risk stratification, pending external validation.
