Association between pathologic grade and multiphase computed tomography enhancement in pancreatic neuroendocrine neoplasm

Kang, Jong Won; Ryu, Jihye; Son, Jun Hyuk; Lee, Jae Woo; Choi, Yong‐Kook; Lee, Sang Hyub; Kim, Yong Tae

doi:10.1111/jgh.14139

Cited by 18 publications

(4 citation statements)

References 28 publications

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“…Pancreatic neuroendocrine tumors (PNETs) are rare neoplasms that arise from pluripotent stem cells in ductal epithelium, accounting for less than 3% of all pancreatic neoplasms 1. However, the incidence of PNETs has increased over the past decade, which may be due to the increased awareness, improved diagnostic imaging modalities, and the revision of its definition 2–4. In the WHO 2017 classification, PNETs are categorized based on the Ki-67 proliferation index and mitotic activity.…”

Section: Introductionmentioning

confidence: 99%

<p>Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance</p>

Guo¹,

Ren²,

Chen³

et al. 2019

CMAR

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Purpose The 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 methods PNETs 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. Results There 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 ( P <0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703–0.989. Conclusion Our 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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Section: Introductionmentioning

confidence: 99%

<p>Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance</p>

Guo¹,

Ren²,

Chen³

et al. 2019

CMAR

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“…Therefore, WHO classification was revised in 2017 [6, 7]. Previous G3 neuroendocrine carcinoma was divided into well-differentiated neuroendocrine tumor (NET G3) and poorly-differentiated neuroendocrine neoplasm (NEC G3), and the latter was further divided into small cell type and large cell type [8].…”

Section: Introductionmentioning

confidence: 99%

Differentiation of hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography

et al. 2019

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Hypovascular pancreatic neuroendocrine tumors (hypo-PNETs) are often misdiagnosed as pancreatic ductal adenocarcinoma (PDAC). However, the treatment options and prognosis of PNETs and PDAC are substantially different. This retrospective study differentiated hypo-PNETs from PDAC using contrast-enhanced CT (CE-CT). Clinical data and CE-CT findings, including tumor location, size, boundary, pancreatic duct dilatation, local invasion or metastases, tumor contrast enhancement, and tumor-to-pancreas enhancement ratio, were compared between 39 PDACs and 18 hypo-PNETs. At CT imaging, hypo-PNETs showed a higher frequency of a well-defined margin and lower frequencies of pancreatic duct dilatation and local invasion or metastasis when compared with PDAC (p < 0.05 for all). The mean attenuation of hypo-PNETs at the arterial and portal venous phase was significantly higher than that of PDAC (p < 0.001, p = 0.003, respectively). Similar results were observed in tumor-to-pancreas enhancement ratio. Tumor attenuation and tumor-to-pancreas enhancement ratio at the arterial phase showed the largest area under the curve (AUC) of 0.888 and 0.812 with 83.3–88.9% of sensitivity and 61.6–77.0% of specificity. Pancreatic duct dilatation, local invasion or metastasis, and tumor attenuation at the portal venous phase also showed acceptable AUC (0.703–0.748). Thus CE-CT features, especially the enhancement degree at the arterial phases, may be useful for differentiating hypo-PNETs from PDAC using CE-CT.

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“…In this study, we analyzed some qualitative radiological features and found that the enhancement degree in the hepatic arterial phase was the only predictor that could be reliably used for the discrimination of LMs of the digestive system NETs from those of NECs. Previous studies relating to the pancreatic NENs have shown that the enhancement degree in the early arterial phase of well-differentiated tumors is significantly higher than in poorly differentiated tumors on enhanced CT. 32,33 In our previous study, the LMs of well-differentiated gastroenteropancreatic-NENs had a higher enhancement degree in the hepatic arterial phase when compared with poorly differentiated gastroenteropancreatic-NENs. 19 The findings of the present study are in line with these previous results.…”

Section: Discussionmentioning

confidence: 88%

Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography–Based Radiomics Analysis

Cui

Zhu

et al. 2023

J Comput Assist Tomogr

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The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs).Methods: Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC).Results: In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621). Conclusions:The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.

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Association between pathologic grade and multiphase computed tomography enhancement in pancreatic neuroendocrine neoplasm

Abstract: Computed tomography enhancement value at early arterial phase and its changing pattern can be a useful predictor for the differentiation of pathologic grade of PanNENs.

Cited by 18 publications

References 28 publications

<p>Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance</p>

<p>Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance</p>

Differentiation of hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography

Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography–Based Radiomics Analysis

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