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Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?

Cesare Maino1, Paolo Niccolò Franco2, Federica Omboni3

  • 1Departement of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy. mainocesare@gmail.com.

Abdominal Radiology (New York)
|April 25, 2026
PubMed
Summary

Artificial intelligence (AI) and radiomics show promise for characterizing pancreatic cystic lesions (PCLs) non-invasively. These advanced imaging techniques aid in differentiating cyst types and identifying high-risk neoplasms, potentially improving patient management.

Keywords:
Artificial intelligenceMachine learningMagnetic resonance imagingPancreatic cystsTomographyX-ray computed

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Pancreatic cystic lesions (PCLs) are increasingly detected, posing diagnostic challenges due to their varied nature, from benign to malignant potential.
  • Accurate characterization and risk stratification are crucial for appropriate management and avoiding unnecessary surgeries.

Purpose of the Study:

  • To review current evidence on CT- and MR-based radiomics and AI for pancreatic cyst characterization.
  • To assess the role of these techniques in differentiating cyst subtypes, identifying high-risk IPMNs, and supporting clinical decisions.

Main Methods:

  • Review of existing literature on CT- and MR-based radiomics and AI applications in PCLs.
  • Focus on quantitative imaging features extraction beyond visual assessment.

Main Results:

  • AI and radiomics demonstrate potential for improved non-invasive characterization of PCLs.
  • These techniques can assist in differentiating mucinous from non-mucinous cysts and identifying high-risk IPMNs.

Conclusions:

  • AI and radiomics offer promising advancements in PCL characterization and risk stratification.
  • Methodological limitations such as data variability, reproducibility, and validation need addressing for clinical integration.
  • Further multicenter studies and prospective validation are essential for routine clinical practice adoption.