ArticleEuropean radiology2019
CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 5 of them syntheses that pooled it.
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Who cites it
91 citing papers in PubMed, 5 syntheses or guidelines pooled it, 173 citations in OpenAlex.
- Diagnostic performance of artificial intelligence versus conventional imaging for differentiating G2/G3 from G1 pancreatic neuroendocrine tumors: a systematic review and meta-analysis.BMC medical imaging · 2026Pooled it
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- GEP-NET radiomics: a systematic review and radiomics quality score assessment.European radiology · 2022Pooled it
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Reproducibility of radiomics quality score: an intra- and inter-rater reliability study.European radiology · 2024Trial
- Multi-omic profiles of neuroendocrine neoplasms of the pancreas: an integrated landscape.Molecular cancer · 2026Review
- Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging.BMC medical informatics and decision making · 2026Article
- Could a multimodal fusion model integrating CT radiomics and systemic inflammatory markers improve preoperative risk stratification of parotid masses? A retrospective exploratory study.Frontiers in immunology · 2026Article
- From diagnosis to treatment: navigating the course for pancreatic neuroendocrine neoplasms.Annals of medicine · 2025Review
- Prediction of Histopathological Grade of Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced Magnetic Resonance Imaging Radiomics Features.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2025Article
- Development and validation of a CT-based nomogram to preoperative prediction of pancreatic neuroendocrine tumors (pNETs) grade.Abdominal radiology (New York) · 2025Article
- Preoperative prediction of pancreatic neuroendocrine tumors grade based on computed tomography, magnetic resonance imaging and endoscopic ultrasonography.Abdominal radiology (New York) · 2025Article
- 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 to preoperatively predict the aggressiveness of pancreatic neuroendocrine tumors based on CT features and 3D CT radiomic features.Abdominal radiology (New York) · 2025Article
- CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study.Insights into imaging · 2025Article
- Article
- Factors associated with grade progression in pancreatic neuroendocrine tumors.Endocrine-related cancer · 2025Article
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
31 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 6 institutions in 1 country.
Funding
Abstract
objectiveTo develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs).
methodsOne hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use.
resultsThe fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.
conclusionWe proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients. KEY POINTS: • Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.
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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.