Evaluating the role of the main pancreatic duct in intraductal papillary mucinous neoplasm grading: A multi-structure radiomics-based machine learning approach

Emir A Syailendra; Felipe Lopez-Ramirez; Florent Tixier; Alejandra Blanco; Hajra Arshad; Mohammad Yasrab; Zahra F Rahmatullah; Ammar A Javed; Jin He; Anne Marie Lennon; Ralph H Hruban; Elham Afghani; Satomi Kawamoto; Linda C Chu; Elliot K Fishman

doi:10.1016/j.diii.2026.02.002

Evaluating the role of the main pancreatic duct in intraductal papillary mucinous neoplasm grading: A multi-structure radiomics-based machine learning approach

Diagn Interv Imaging. 2026 Feb 19:S2211-5684(26)00027-6. doi: 10.1016/j.diii.2026.02.002. Online ahead of print.

Authors

Affiliations

¹ Russel H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA. Electronic address: msyaile1@jhu.edu.
² Russel H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA.
³ Department of Surgery, New York University Grossman School of Medicine, New York, NY 10016, USA.
⁴ Department of Surgery, School of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA.
⁵ Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.
⁶ Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, School of Medicine, Johns Hopkins University Baltimore, MD 21287, USA.
⁷ Division of Gastroenterology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA.

PMID: 41720652
DOI: 10.1016/j.diii.2026.02.002

Abstract

Purpose: The purpose of this study was to evaluate the contribution of radiomics features extracted from various pancreatic structures on computed tomography (CT) images, including the main pancreatic duct and cystic lesion, for predicting the pathological grade of intraductal papillary mucinous neoplasms (IPMNs) using machine learning models.

Materials and methods: A retrospective study using preoperative CT images obtained during the venous phase of enhancement in patients with pathologically confirmed IPMNs (2003-2024) was conducted. Main pancreatic ducts and cysts were manually segmented. Machine learning models were trained to classify IPMNs into high-grade/associated invasive carcinoma (HG/I) IPMNs or low-grade (LG) IPMNs using radiomics features from three structures (i.e., cysts, main pancreatic duct, and a combination of both structures). Model performance was evaluated using area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) values were used to interpret feature importance.

Results: A total of 274 patients with IPMNs were included. There were 149 patients with HG/I IPMNs (70 women [47 %]; median age, 71.0 years; age range: 29-92) and 125 patients with LG IPMNs (73 women [58.4 %]; median age, 68.0 years; range: 35-86). HG/I IPMNs were predominantly mixed-type IPMNs (51.7 %; 77/149). LG IPMNs were unspecified (51/125; 40.8 %), main/mixed (40/125; 32 %), or branch-duct type (34/125; 27.2 %). A support vector machine trained on combined features achieved the largest AUC (0.85; 95 % confidence interval [CI]: 0.85-0.87; P < 0.001), with 90 % sensitivity (95 % CI: 90-93), and 60 % specificity (95 % CI: 58-62). SHAP analysis identified main pancreatic duct radiomics features as having the largest contribution to model output.

Conclusion: Integrating CT-based radiomics features from pancreatic ducts and cysts improves classification performance, with main pancreatic duct features being the most contributive predictor of IPMN grade.

Keywords: Artificial intelligence; Intraductal papillary mucinous neoplasm; Machine learning; Main pancreatic duct; Radiomics; Risk stratification.