ArticleDigestive diseases and sciences2018
Simple Vascular Architecture Classification in Predicting Pancreatic Neuroendocrine Tumor Grade and Prognosis.
Article in Digestive diseases and sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 14 citations in OpenAlex.
- Multi-label dynamic diagnosis of pancreatic diseases using AI-enhanced endoscopic ultrasound: a multi-cohort real-world study.Surgical endoscopy · 2026Article
- Echoendoscopic evaluation of the vascular pattern of solid pancreatic lesions as a predictor of neuroendocrine neoplasms: a retrospective study in Mexico.Clinical endoscopy · 2026Article
- Does grade matter? Evaluation of histologic grade in patients with stage 1 non-small cell lung cancer using the National Cancer Database.JTCVS open · 2025Article
- Usefulness of endoscopic ultrasonography for differentiating between non-functional pancreatic neuroendocrine neoplasm and intrapancreatic accessory spleen.Scientific reports · 2025Article
- Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms-A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Increased carbohydrate antigen 19-9 expression in a thymic neuroendocrine tumor.Thoracic cancer · 2021Article
- Supervised Machine Learning in Oncology: A Clinician's Guide.Digestive disease interventions · 2020Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimVascularity is a critical feature in the evaluation of pancreatic neuroendocrine tumor (PNET). When done by EUS, contrast agents are recommended. However, vascular architecture (VA) can also be evaluated by routine Doppler flow in EUS without contrast agents. Our aim was to provide a simple VA classification in EUS for PNET grade and prognosis.
methodsAll pathologically proven PNET cases with EUS between 2012 and 2018 were retrospectively analyzed. The Doppler imaging was retrieved for VA classification. Predictive model construction was performed by machine learning algorithms.
resultsA total of 112 PNET cases were evaluated, among which 93 cases were subjected to VA classification. The VA was classified into type A (peritumoral with or without intratumoral vessels [A1 or A2]); type B (only intratumoral vessels); and type C (flow was absent). The VA classification was significantly correlated with tumor grades: 74% type A1 was G1, 73% type B was G2, and 58% type C was G3. Multivariate analysis indicated that elevated serum CA19-9 and type C classification were the independent predictors of G3 tumor. Five machine learning models were constructed, among which random forest was the best one with an AUC of 0.9972. Low-risk patients classified by this model exhibited better prognosis than high-risk patients (p = 0.0087).
conclusionsIn the novel simple VA classification, peritumoral, intratumoral, and absent vessels are prone to be G1, G2, and G3, respectively. Combined with serum CA19-9 and lesion size, the VA classification could predict tumor grade and prognosis in PNET.
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