ArticleFrontiers in oncology2021
Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Image-based artificial intelligence for preoperative differentiation of pancreatic cancer from pancreatitis: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Diagnostic Imaging of Pancreatic and Biliary Involvement in IgG4-Related Disease: Key Imaging Features, Diagnostic Criteria and Differential Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Enhancing preoperative diagnosis of pancreatic ductal adenocarcinoma and mass-forming chronic pancreatitis: a study on normalized conventional MR imaging parameters.Abdominal radiology (New York) · 2026Article
- Development and validation of a clinical-radiomics nomogram for the differential diagnosis of focal pancreatic solid lesions: a retrospective cohort study.Translational cancer research · 2026Article
- Artificial intelligence in chronic and autoimmune pancreatitis: diagnosis, prognosis, and personalized management.Frontiers in medicine · 2026Review
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- A nomogram based on multiparametric magnetic resonance imaging radiomics for prediction of acute pancreatitis activity.BMC medical imaging · 2025Article
- Article
- Machine Learning-Based Radiomics for Differentiating Pancreatic Lesions: A Potential Tool to Enhance Clinical Decision-Making and Nursing Management.Journal of nursing management · 2025Article
- Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography.Diagnostics (Basel, Switzerland) · 2024Article
- Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.Diagnostics (Basel, Switzerland) · 2024Review
- Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma.Insights into imaging · 2023Article
- Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions.Journal of clinical medicine · 2023Review
- Review
- Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis.Bioengineering (Basel, Switzerland) · 2023Article
- Development and validation of an ultrasound-based prediction model for differentiating between malignant and benign solid pancreatic lesions.European radiology · 2022Article
- A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability.Insights into imaging · 2022Article
- Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.Cancers · 2022Review
Corrections and comments
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Authors and funding
10 authors.
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Abstract
backgroundIt is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions.
methodsThis retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists' evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination.
resultsThe areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists' evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved.
conclusionsThe radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.
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