ArticleFrontiers in oncology2021
Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR.
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 7 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Review
- Preoperative prediction of pancreatic neuroendocrine tumors grade based on computed tomography, magnetic resonance imaging and endoscopic ultrasonography.Abdominal radiology (New York) · 2025Article
- Differentiation between G3 pancreatic neuroendocrine tumor and pancreatic neuroendocrine carcinoma based on intratumor and peritumor CT value ratio and abnormal vascular network.Frontiers in oncology · 2025Article
- Neuroendocrine Carcinoma of the Urinary Bladder: CT Findings and Radiomics Signature.Journal of clinical medicine · 2023Article
- A Machine Learning Approach Using [Molecular imaging and biology · 2023Article
- The associations between serum high-density lipoprotein cholesterol levels and malignant behavior in pancreatic neuroendocrine neoplasms.Lipids in health and disease · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images. MATERIALS AND
methodsTotally 48 patients but 51 lesions with a pathological tumor grade were subdivided into low grade (G1) group and intermediate grade (G2) group. The ROI was manually segmented slice by slice in 3D-T1 weighted sequence with and without enhancement. Statistical differences of radiomic features between G1 and G2 groups were analyzed using the independent sample
resultsNo significant difference between G1 and G2 groups (
conclusionsThe maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.
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