Background: Pancreatic adenocarcinoma is one of the most aggressive and lethal cancers, with a poor prognosis primarily due to late-stage diagnosis. Improving the accuracy of pancreatic cancer diagnosis is crucial for enhancing survival outcomes, yet the sensitivity of conventional diagnostic methods remains a significant challenge. This study aims to evaluate the effectiveness of radiomics features extracted from Computed Tomography (CT) imaging, combined with machine learning models, for the detection of pancreatic adenocarcinoma. Methods: A retrospective dataset from Baqiyatallah Hospital, Tehran, Iran (2024) of 100 participants (50 with pancreatic adenocarcinoma (primarily stages II-III) and 50 healthy controls) was used. CT images were acquired with a three-phase protocol, and radiomics features were extracted using 3D Slicer software. Three classifiers-Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF)-were employed, with feature selection methods including Recursive Feature Elimination (RFE), Mutual Information (MI), and Least Absolute Shrinkage and Selection Operator (LASSO). Model performance was assessed using accuracy, precision, sensitivity, F1 score, and area under the curve (AUC). Results: The SVM classifier with LASSO feature selection achieved the highest performance, with an accuracy of 0.83 and an AUC of 0.89. LR and RF also demonstrated strong results, with LASSO providing the best feature selection for both classifiers. SHAP analysis revealed that textural features such as gray-level-non-uniformity and run-length-non-uniformity were the most important drivers for distinguishing pancreatic cancer from normal tissue. Conclusion: Radiomics-based machine learning models show promise for improving the diagnosis of pancreatic adenocarcinoma. The combination of LASSO and powerful classifiers such as SVM, LR, and RF offers a robust framework for non-invasive, accurate diagnostic tools.
Talebi et al. (Sun,) studied this question.