ArticleEuropean radiology2021
Radiomics model of dual-time 2-[
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 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
- Diagnostic performance of F-18 FDG PET/CT in differentiating autoimmune pancreatitis from pancreatic cancer: a systemic review and meta-analysis.Annals of nuclear medicine · 2024Pooled it
- Diagnostic performance of ¹⁸F-FDG PET/CT radiomics-based machine learning models for pancreatic lesion characterisation: Comparison with visual assessment and evaluation of human-radiomics synergy.European journal of nuclear medicine and molecular imaging · 2026Article
- Diagnostic Imaging of Pancreatic and Biliary Involvement in IgG4-Related Disease: Key Imaging Features, Diagnostic Criteria and Differential Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in chronic and autoimmune pancreatitis: diagnosis, prognosis, and personalized management.Frontiers in medicine · 2026Review
- Diagnostic methods for pancreatic cancer and their clinical applications (Review).Oncology letters · 2025Review
- Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based onAnnals of nuclear medicine · 2025Article
- AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.Abdominal radiology (New York) · 2025Review
- An update on pancreatic cancer imaging, staging, and use of the PACT-UK radiology template pre- and post-neoadjuvant treatment.The British journal of radiology · 2025Review
- Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography.Diagnostics (Basel, Switzerland) · 2024Article
- Semisupervised 3D segmentation of pancreatic tumors in positron emission tomography/computed tomography images using a mutual information minimization and cross-fusion strategy.Quantitative imaging in medicine and surgery · 2024Article
- Article
- A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based onJapanese journal of radiology · 2023Article
- A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma.Frontiers in oncology · 2023Article
- Distinction of Lymphoma from Sarcoidosis onJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Article
- A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability.Insights into imaging · 2022Article
- Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.Healthcare (Basel, Switzerland) · 2022Review
- Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.Cancers · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectivesPancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP) are diseases with a highly analogous visual presentation that are difficult to distinguish by imaging. The purpose of this research was to create a radiomics-based prediction model using dual-time PET/CT imaging for the noninvasive classification of PDAC and AIP lesions.
methodsThis retrospective study was performed on 112 patients (48 patients with AIP and 64 patients with PDAC). All cases were confirmed by imaging and clinical follow-up, and/or pathology. A total of 502 radiomics features were extracted from the dual-time PET/CT images to develop a radiomics decision model. An additional 12 maximum intensity projection (MIP) features were also calculated to further improve the radiomics model. The optimal radiomics feature set was selected by support vector machine recursive feature elimination (SVM-RFE), and the final classifier was built using a linear SVM. The performance of the proposed dual-time model was evaluated using nested cross-validation for accuracy, sensitivity, specificity, and area under the curve (AUC).
resultsThe final prediction model was developed from a combination of the SVM-RFE and linear SVM with the required quantitative features. The multimodal and multidimensional features performed well for classification (average AUC: 0.9668, accuracy: 89.91%, sensitivity: 85.31%, specificity: 96.04%).
conclusionsThe radiomics model based on 2-[ KEY POINTS: • The clinical symptoms and imaging visual presentations of PDAC and AIP are highly similar, and accurate differentiation of PDAC and AIP lesions is difficult. • Radiomics features provided a potential noninvasive method for differentiation of AIP from PDAC. • The diagnostic performance of the proposed radiomics model indicates its potential to assist doctors in making treatment decisions.
Indexed as
Identifiers
33677645What OpenQuestion holds
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.