ArticleScientific reports2019
CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- 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
- Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound.BMC medical informatics and decision making · 2025Article
- Preoperative prediction of pancreatic neuroendocrine tumors grade based on computed tomography, magnetic resonance imaging and endoscopic ultrasonography.Abdominal radiology (New York) · 2025Article
- Radiomics in differential diagnosis of pancreatic tumors.European journal of radiology open · 2025Article
- Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 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
- Virtual Tumor Mapping: A New Standard for Surgeon-Pathologist Collaboration in Treating Oral Squamous Cell Carcinoma.Cancers · 2024Article
- Article
- Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients.Annals of surgical oncology · 2024Article
- Baseline radiologic features as predictors of efficacy in patients with pancreatic neuroendocrine tumors with liver metastases receiving surufatinib.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2023Article
- A Machine Learning Approach Using [Molecular imaging and biology · 2023Article
- Radiomics and outcome prediction to antiangiogenic treatment in advanced gastroenteropancreatic neuroendocrine tumours: findings from the phase II TALENT trial.BJC reports · 2023Article
- Prognostic value of tumor-to-parenchymal contrast enhancement ratio on portal venous-phase CT in pancreatic neuroendocrine neoplasms.European radiology · 2023Article
- Qualitative imaging features of pancreatic neuroendocrine neoplasms predict histopathologic characteristics including tumor grade and patient outcome.Abdominal radiology (New York) · 2022Article
- 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
- Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms.World journal of gastroenterology · 2022Review
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors.
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