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Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A

Neus Torra-Ferrer¹, Maria Montserrat Duh¹, Queralt Grau-Ortega²

¹Department of Radiology, Hospital of Mataró (Consorci Sanitari del Maresme), C/ Cirera 230, 08304 Mataró, Spain.

Journal of Imaging

|March 26, 2025

Summary

Machine learning effectively categorizes pancreatic cystic lesions (PCLs) into mucinous and non-mucinous types. This radiomics software tool shows high accuracy, improving diagnostic capabilities for PCLs.

Keywords:

computer vision deep features feature extraction image processing machine learning pancreatic cystic lesions radiomics

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

High-resolution imaging increases pancreatic cystic lesion (PCL) detection.
Accurate classification of PCLs (IPMN, MCN, SCN) remains a diagnostic challenge.

Purpose of the Study:

Develop and validate a radiomics-based machine learning (ML) tool for PCL classification.
Distinguish between mucinous and non-mucinous PCL types.

Main Methods:

Utilized a dataset of 261 CT examinations (156 training, 105 external validation).
Extracted 38 radiological and 214 radiomic features using Pyradiomics.
Applied LASSO regression for feature selection and AdaBoost for classification.

Main Results:

Achieved 89.3% accuracy in internal validation.
External validation showed 90.2% sensitivity, 80% specificity, and 88.2% overall accuracy.
Outperformed or matched state-of-the-art radiomics methods, especially in external validation.

Conclusions:

Radiomics-driven ML shows significant potential for enhancing PCL diagnosis.
The developed tool offers improved classification accuracy across diverse populations.
Facilitates more precise differentiation of PCL subtypes.