Applicability of Radiomics for Differentiation of Pancreatic Adenocarcinoma from Healthy Tissue of Pancreas by Using Magnetic Resonance Imaging and Machine Learning.

Authors

Dimitrije Sarac, Milica Badza Atanasijevic, Milica Mitrovic Jovanovic, Jelena Kovac, Ljubica Lazic, Aleksandra Jankovic, Dusan J Saponjski, Stefan Milosevic, Katarina Stosic, Dragan Masulovic, Dejan Radenkovic, Veljko Papic, Aleksandra Djuric-Stefanovic

Published in

Cancers. Volume 17. Issue 7. Mar 27, 2025. Epub Mar 27, 2025.

Abstract

This study analyzed different classifier models for differentiating pancreatic adenocarcinoma from surrounding healthy pancreatic tissue based on radiomic analysis of magnetic resonance (MR) images.
We observed T2W-FS and ADC images obtained by 1.5T-MR of 87 patients with histologically proven pancreatic adenocarcinoma for training and validation purposes and then tested the most accurate predictive models that were obtained on another group of 58 patients. The tumor and surrounding pancreatic tissue were segmented on three consecutive slices, with the largest area of interest (ROI) of tumor marked using MaZda v4.6 software. This resulted in a total of 261 ROIs for each of the observed tissue classes in the training-validation group and 174 ROIs in the testing group. The software extracted a total of 304 radiomic features for each ROI, divided into six categories. The analysis was conducted through six different classifier models with six different feature reduction methods and five-fold subject-wise cross-validation.
In-depth analysis shows that the best results were obtained with the Random Forest (RF) classifier with feature reduction based on the Mutual Information score (all nine features are from the co-occurrence matrix): an accuracy of 0.94/0.98, sensitivity of 0.94/0.98, specificity of 0.94/0.98, and F1-score of 0.94/0.98 were achieved for the T2W-FS/ADC images from the validation group, retrospectively. In the testing group, an accuracy of 0.69/0.81, sensitivity of 0.86/0.82, specificity of 0.52/0.70, and F1-score of 0.74/0.83 were achieved for the T2W-FS/ADC images, retrospectively.
The machine learning approach using radiomics features extracted from T2W-FS and ADC achieved a relatively high sensitivity in the differentiation of pancreatic adenocarcinoma from healthy pancreatic tissue, which could be especially applicable for screening purposes.

PMID:
40227615
Bibliographic data and abstract were imported from PubMed on 14 Apr 2025.

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