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Talebi, Amin, Akhavan Moghadam, Jamal, Sepandi, Mojtaba, Chatab Mohammadi, Taha, Rahmatizadeh, Ali, Shankayi, Zeinab. (1404). Radiomics-Driven Machine Learning Models for Diagnosis of Pancreatic Adenocarcinoma. سامانه مدیریت نشریات علمی, 51(3), 175-185. doi: 10.30476/ijms.2025.105971.4207
Amin Talebi; Jamal Akhavan Moghadam; Mojtaba Sepandi; Taha Chatab Mohammadi; Ali Rahmatizadeh; Zeinab Shankayi. "Radiomics-Driven Machine Learning Models for Diagnosis of Pancreatic Adenocarcinoma". سامانه مدیریت نشریات علمی, 51, 3, 1404, 175-185. doi: 10.30476/ijms.2025.105971.4207
Talebi, Amin, Akhavan Moghadam, Jamal, Sepandi, Mojtaba, Chatab Mohammadi, Taha, Rahmatizadeh, Ali, Shankayi, Zeinab. (1404). 'Radiomics-Driven Machine Learning Models for Diagnosis of Pancreatic Adenocarcinoma', سامانه مدیریت نشریات علمی, 51(3), pp. 175-185. doi: 10.30476/ijms.2025.105971.4207
Talebi, Amin, Akhavan Moghadam, Jamal, Sepandi, Mojtaba, Chatab Mohammadi, Taha, Rahmatizadeh, Ali, Shankayi, Zeinab. Radiomics-Driven Machine Learning Models for Diagnosis of Pancreatic Adenocarcinoma. سامانه مدیریت نشریات علمی, 1404; 51(3): 175-185. doi: 10.30476/ijms.2025.105971.4207

Radiomics-Driven Machine Learning Models for Diagnosis of Pancreatic Adenocarcinoma

Iranian Journal of Medical Sciences
مقاله 2، دوره 51، شماره 3، خرداد 2026، صفحه 175-185    اصل مقاله (1.55 M)
نوع مقاله: Original Article(s)
شناسه دیجیتال (DOI): 10.30476/ijms.2025.105971.4207
نویسندگان
Amin Talebi1؛ Jamal Akhavan Moghadam2؛ Mojtaba Sepandi3؛ Taha Chatab Mohammadi4؛ Ali Rahmatizadeh4؛ Zeinab Shankayi* 1، 5
1Department of Physiology and Medical Physics, School of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran
2Department of Surgery, School of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran
3Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
4Student Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran
5Radiation Sciences Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
چکیده
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.

تازه های تحقیق

Amin Talebi (Google Scholar)
Zeinab Shankayi (Google Scholar)

کلیدواژه‌ها
Pancreatic neoplasms؛ Radiomics؛ Machine learning؛ Diagnosis؛ Tomography
مراجع
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