Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis

Pancreatic neuroendocrine tumors (p-NETs) are a rare group of neoplasms that often present with liver metastases. Histological characteristics, metabolic behavior, and liver tumor burden (LTB) are important prognostic factors. In this study, the usefulness of texture analysis of liver metastases in evaluating the biological aggressiveness of p-NETs was assessed. Fifty-six patients with liver metastases from p-NET were retrospectively enrolled. Qualitative and quantitative CT features of LTB were evaluated. Histogram-derived parameters of liver metastases were calculated and correlated with the tumor grade (G) and 18F-fluorodeoxyglucose (18F-FDG) standardized uptake value (SUV). Arterial relative enhancement was inversely related with G (−0.37, p = 0.006). Different metastatic spread patterns of LTB were not associated with histological grade. Arterialentropy was significantly correlated to G (−0.368, p = 0.038) and to Ki67 percentage (−0.421, p = 0.018). The ROC curve for the Arterialentropy reported an area under the curve (AUC) of 0.736 (95% confidence interval 0.545–0.928, p = 0.035) in the identification of G1–2 tumors. Arterialuniformity values were correlated to G (0.346, p = 0.005) and Ki67 levels (0.383, p = 0.033). Arterialentropy values were directly correlated with the SUV (0.449, p = 0.047) which was inversely correlated with Arterialuniformity (−0.499, p = 0.025). Skewness and kurtosis reported no significant correlations. In conclusion, histogram-derived parameters may predict adverse histological features and metabolic behavior of p-NET liver metastases.

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Staging accuracy of MR for pancreatic neuroendocrine tumor and imaging findings according to the tumor grade

Abdominal Imaging ◽

10.1007/s00261-013-0011-y ◽

2013 ◽

Vol 38 (5) ◽

pp. 1106-1114 ◽

Cited By ~ 32

Author(s):

Jung Hoon Kim ◽

Hyo Won Eun ◽

Young Jae Kim ◽

Joon Koo Han ◽

Byung Ihn Choi

Keyword(s):

Neuroendocrine Tumor ◽

Pancreatic Neuroendocrine Tumor ◽

Tumor Grade ◽

Imaging Findings ◽

Staging Accuracy

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Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis

Acta Radiologica ◽

10.1177/0284185117725367 ◽

2017 ◽

Vol 59 (4) ◽

pp. 383-392 ◽

Cited By ~ 42

Author(s):

Tae Won Choi ◽

Jung Hoon Kim ◽

Mi Hye Yu ◽

Sang Joon Park ◽

Joon Koo Han

Keyword(s):

Texture Analysis ◽

Three Dimensional ◽

Multivariate Logistic Regression Analysis ◽

Pancreatic Neuroendocrine Tumor ◽

Tumor Grade ◽

3D Analysis ◽

Variable Degree ◽

Portal Phase ◽

Ct Findings ◽

Contrast Enhanced Ct

Background Pancreatic neuroendocrine tumors (PNET) include heterogeneous tumors with a variable degree of inherent biologic aggressiveness represented by the histopathologic grade. Although several studies investigated the computed tomography (CT) characteristics which can predict the histopathologic grade of PNET, accurate prediction of the PNET grade by CT examination alone is still limited. Purpose To investigate the important CT findings and CT texture variables for prediction of grade of PNET. Material and Methods Sixty-six patients with pathologically confirmed PNETs (grade 1 = 45, grades 2/3 = 21) underwent preoperative contrast-enhanced CT. Two reviewers determined the presence of predefined CT findings. CT texture was also analyzed on arterial and portal phase using both two-dimensional (2D) and three-dimensional (3D) analysis. Multivariate logistic regression analysis was performed in order to identify significant predictors for tumor grade. Results Among CT findings and CT texture variables, the significant predictors for grade 2/3 tumors were an ill-defined margin (odds ratio [OR] = 7.273), lower sphericity (OR = 0.409) on arterial 2D analysis, higher skewness (OR = 1.972) and lower sphericity (OR = 0.408) on arterial 3D analysis, lower kurtosis (OR = 0.436) and lower sphericity (OR = 0.420) on portal 2D analysis, and a larger surface area (OR = 2.007) and lower sphericity (OR = 0.503) on portal 3D analysis ( P < 0.05). Diagnostic performance of texture analysis was superior to CT findings (AUC = 0.774 vs. 0.683). Conclusion CT is useful for predicting grade 2/3 PNET using not only the imaging findings including an ill-defined margin, but also the CT texture variables such as lower sphericity, higher skewness, and lower kurtosis.

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