ArticleCancer management and research2019
Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance.
Article in Cancer management and research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 4 syntheses or guidelines pooled 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
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- Impact of functionality and grading on survival in pancreatic neuroendocrine tumor patients receiving peptide receptor radionuclide therapy.Frontiers in endocrinology · 2025Article
- Quantitative evaluation of pancreatic neuroendocrine tumors utilizing dual-source CT perfusion imaging.BMC medical imaging · 2024Article
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- Computed tomography features in prediction of histological differentiation of pancreatic neuroendocrine neoplasms - a single-centre retrospective cohort study.Polish journal of radiology · 2024Article
- Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature.Diagnostic and interventional imaging · 2024Article
- A Machine Learning Approach Using [Molecular imaging and biology · 2023Article
- Diagnostic accuracy of apparent diffusion coefficient to differentiate intrapancreatic accessory spleen from pancreatic neuroendocrine tumors.World journal of gastrointestinal oncology · 2023Article
- Diagnostic performance of texture analysis in the differential diagnosis of perianal fistulising Crohn's disease and glandular anal fistula.World journal of gastrointestinal surgery · 2023Article
- Maximum Value on Arterial Phase Computed Tomography Predicts Prognosis and Treatment Efficacy of Sunitinib for Pancreatic Neuroendocrine Tumours.Annals of surgical oncology · 2023Article
- Spectral Computed Tomography-Derived Iodine Content and Tumor Response in the Follow-Up of Neuroendocrine Tumors-A Single-Center Experience.Current oncology (Toronto, Ont.) · 2023Article
- Pancreatic neuroendocrine neoplasms: a 2022 update for radiologists.Abdominal radiology (New York) · 2022Review
- Update on quantitative radiomics of pancreatic tumors.Abdominal radiology (New York) · 2022Review
- Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.Healthcare (Basel, Switzerland) · 2022Review
- Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors.Journal of ovarian research · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance. PATIENTS AND
methodsPNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher's exact tests or Kruskal-Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades.
resultsThere were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (
conclusionOur data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.
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