Non-invasive PNET grading using CT radiomics and machine learning
Pancreatic cancer is a major cause of cancer-related fatalities globally, with a poor prognosis. Machine learning-based medical image analysis has emerged as a promising approach for improving clinical decision-making. The purpose is to determine the most effective machine learning method and phase...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2025.2500429 |
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| author | Faeze Salahshour Mahsa Taherzadeh Ghasem Hajianfar Gholamreza Bayat Farid Azmoudeh Ardalan Soroush Bagheri Arman Esmailzadeh Majid Kahe Sajad P. Shayesteh |
| author_facet | Faeze Salahshour Mahsa Taherzadeh Ghasem Hajianfar Gholamreza Bayat Farid Azmoudeh Ardalan Soroush Bagheri Arman Esmailzadeh Majid Kahe Sajad P. Shayesteh |
| author_sort | Faeze Salahshour |
| collection | DOAJ |
| description | Pancreatic cancer is a major cause of cancer-related fatalities globally, with a poor prognosis. Machine learning-based medical image analysis has emerged as a promising approach for improving clinical decision-making. The purpose is to determine the most effective machine learning method and phase of CT scan to provide clinicians with an efficient tool for accurately identifying pathological grades of pancreatic neuroendocrine tumours (PNET). This will be achieved by analysing contrast-enhanced computed tomography scans of both arterial and portal phases. An investigation was conducted on a cohort of 100 patients diagnosed with pancreatic neuroendocrine tumours. Radiomic features were extracted using Pyradiomics. These features were subsequently utilised in different machine learning classifiers. The classification model’s performance was assessed using sensitivity, specificity, area under the curve (AUC) and accuracy metrics. Our analysis demonstrates that combining CT-based radiomic features with a machine-learning approach can identify the pathological grades of pancreatic neuroendocrine tumours. the combination of Portal_RFE and K-Nearest Neighbour (KNN) demonstrated the highest predictive performance with an AUC of 0.76 and 0.69 in training and validation models, respectively. The use of CT radiomic features and machine learning effectively determines PNET pathological grades, aiding in classifying patients for clinical decisions. |
| format | Article |
| id | doaj-art-22a2a3959676421fb6ea4c0803abce52 |
| institution | OA Journals |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-22a2a3959676421fb6ea4c0803abce522025-08-20T02:14:03ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712025-12-0113110.1080/21681163.2025.2500429Non-invasive PNET grading using CT radiomics and machine learningFaeze Salahshour0Mahsa Taherzadeh1Ghasem Hajianfar2Gholamreza Bayat3Farid Azmoudeh Ardalan4Soroush Bagheri5Arman Esmailzadeh6Majid Kahe7Sajad P. Shayesteh8Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences (TUMS), Tehran, IranDepartment of Radiology, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, IranRajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, IranDepartment of Physiology, Pharmacology and Medical Physic, School of Medicine, Alborz University of Medical Sciences, Karaj, IranDepartment of Pathology, School of Medicine, Liver Transplantation Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, IranDepartment of Medical Physics, Kashan University of Medical Sciences, Kashan, IranDepartment of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, IranDepartment of Radiation Oncology, Department of Internal Medicine, School of Medicine, Imam Ali Hospital, Alborz University of Medical Sciences, Karaj, IranDepartment of Physiology, Pharmacology and Medical Physic, School of Medicine, Alborz University of Medical Sciences, Karaj, IranPancreatic cancer is a major cause of cancer-related fatalities globally, with a poor prognosis. Machine learning-based medical image analysis has emerged as a promising approach for improving clinical decision-making. The purpose is to determine the most effective machine learning method and phase of CT scan to provide clinicians with an efficient tool for accurately identifying pathological grades of pancreatic neuroendocrine tumours (PNET). This will be achieved by analysing contrast-enhanced computed tomography scans of both arterial and portal phases. An investigation was conducted on a cohort of 100 patients diagnosed with pancreatic neuroendocrine tumours. Radiomic features were extracted using Pyradiomics. These features were subsequently utilised in different machine learning classifiers. The classification model’s performance was assessed using sensitivity, specificity, area under the curve (AUC) and accuracy metrics. Our analysis demonstrates that combining CT-based radiomic features with a machine-learning approach can identify the pathological grades of pancreatic neuroendocrine tumours. the combination of Portal_RFE and K-Nearest Neighbour (KNN) demonstrated the highest predictive performance with an AUC of 0.76 and 0.69 in training and validation models, respectively. The use of CT radiomic features and machine learning effectively determines PNET pathological grades, aiding in classifying patients for clinical decisions.https://www.tandfonline.com/doi/10.1080/21681163.2025.2500429Pancreatic neuroendocrine tumorsradiomicsCTpathological gradingmachine learning |
| spellingShingle | Faeze Salahshour Mahsa Taherzadeh Ghasem Hajianfar Gholamreza Bayat Farid Azmoudeh Ardalan Soroush Bagheri Arman Esmailzadeh Majid Kahe Sajad P. Shayesteh Non-invasive PNET grading using CT radiomics and machine learning Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Pancreatic neuroendocrine tumors radiomics CT pathological grading machine learning |
| title | Non-invasive PNET grading using CT radiomics and machine learning |
| title_full | Non-invasive PNET grading using CT radiomics and machine learning |
| title_fullStr | Non-invasive PNET grading using CT radiomics and machine learning |
| title_full_unstemmed | Non-invasive PNET grading using CT radiomics and machine learning |
| title_short | Non-invasive PNET grading using CT radiomics and machine learning |
| title_sort | non invasive pnet grading using ct radiomics and machine learning |
| topic | Pancreatic neuroendocrine tumors radiomics CT pathological grading machine learning |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2025.2500429 |
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