Radiomics-Based CT Analysis for Diagnosis and Staging of Chronic Pancreatitis
Author Block: S. Nalliah1, S. N. F. Hostrup1, E. B. Mark1, M. H. Liedenbaum2, T. Engjom2, I. H. S. Haldorsen2, A. M. Drewes1, S. S. Olesen1, J. B. Frøkjær1; 1Aalborg/DK, 2Bergen/NO
Purpose: To evaluate whether a radiomics-based AI model can (1) classify patients with chronic pancreatitis (CP), (2) identify CP-related complications, and (3) provide quantitative imaging biomarkers of disease severity on routine CT.
Methods or Background: CP is a progressive inflammatory disease associated with pain, diabetes, and exocrine pancreatic insufficiency (EPI). Conventional imaging typically detects advanced disease, limiting early diagnosis and accurate staging. Radiomics can reveal subtle imaging alterations beyond visual assessment, offering an opportunity for earlier detection and monitoring. We evaluated whether CT-based radiomics could classify CP, detect complications, and provide biomarkers of disease severity. The study included 468 participants: a training cohort of 359 from Aalborg (201 CP, 148 controls) and a test cohort of 109 (68 external CP patients from Bergen, 41 Aalborg controls). CT scans underwent automated pancreatic segmentation, followed by radiomics feature extraction (PyRadiomics) and feature selection using LASSO regression. AI models were trained to classify CP and complications (EPI, diabetes, pain). Performance was evaluated on the test cohort, and model probability scores were correlated with fecal elastase levels and disease severity.
Results or Findings: Forty-six radiomics features were associated with CP and its complications, including markers related to pancreatic volume, calcifications, and ductal dilatation. The CP vs. healthy classification model achieved excellent performance (AUC = 0.97). For complications, AUCs were 0.80 for EPI, 0.63 for diabetes, and 0.59 for pain. Model probability scores correlated with fecal elastase levels (p < 0.001) and increased with disease severity (p = 0.004).
Conclusion: Radiomics-based CT analysis allows accurate CP classification and provides quantitative markers of disease severity and complications. These results support its potential as a non-invasive tool for diagnosis, staging, and longitudinal monitoring of CP
Limitations: Retrospective design, and no prospective clinical validation limit this study.
Funding for this study: The study was funded from the North Denmark Region Health Innovation Fund (journal number: 2024-0015).
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: This retrospective, multi-center study was approved by the Danish National Committee on Health Research Ethics (Ref: 2308560) and The Regional Ethical Committee in Western Norway (Ref: 2019/1037), with data-sharing agreements between Aalborg University Hospital and Haukeland University Hospital approved by the Scandinavian Baltic Pancreas Club (SBPC) steering committee.