SynthesisEuropean radiology2021
Pancreas image mining: a systematic review of radiomics.
Synthesis in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 of them syntheses that pooled it.
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Who cites it
56 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Validation of magnetic resonance imaging for quantification of intrapancreatic fat deposition using phantom and histologic comparators: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Artificial intelligence-based models for quantification of intra-pancreatic fat deposition and their clinical relevance: a systematic review of imaging studies.European radiology · 2026Pooled it
- Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.European radiology · 2023Pooled it
- Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis.Abdominal radiology (New York) · 2026Article
- Evaluation of cystic neoplasms using EUS, MRI, and CT scan and treatment strategy.Annals of medicine and surgery (2012) · 2026Review
- Exploratory PET/CT Radiomics for Predicting Early Progression in Locally Advanced Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Identification of Common Blood Metabolic Derangements Using Magnetic Resonance Signatures of the Pancreas and Liver.Diabetes, obesity & metabolism · 2026Article
- Transferability of radiomics models between deep learning and conventional CT reconstruction algorithms: A task-based assessment for stratifying acute pancreatitis severity.Journal of applied clinical medical physics · 2026Article
- Research Progress on the Application of Radiomics and Deep Learning in Liver Fibrosis.Journal of imaging · 2026Review
- 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
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast-Enhanced CT Imaging.Journal of imaging informatics in medicine · 2025Article
- Developments in MRI radiomics research for vascular cognitive impairment.Insights into imaging · 2025Review
- Novel CT radiomics models for the postoperative prediction of early recurrence of resectable pancreatic adenocarcinoma: A single-center retrospective study in China.Journal of applied clinical medical physics · 2025Article
- Review
- Intra-Pancreatic Fat Deposition and Pancreatitis: Insights from the COSMOS Program.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Review
- Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma.Advances in radiation oncology · 2025Article
- Prediction of acute pancreatitis severity based on early CT radiomics.BMC medical imaging · 2024Article
- Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.Biomedical engineering online · 2024Article
- Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo systematically review published studies on the use of radiomics of the pancreas.
methodsThe search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study.
resultsA total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003).
conclusionsRadiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice. KEY POINTS: • Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.