Haitao Sun1,2, Jianjun Zhou3, Kai Liu3, Tingting Shen3, Xingxing Wang4, Xiaolin Wang5,6. 1. Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. 2. Department of Interventional Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. 3. Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. 4. Department of Pathology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. 5. Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. fduwangxiaolin@hotmail.com. 6. Department of Interventional Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. fduwangxiaolin@hotmail.com.
Abstract
PURPOSES: Predictive factors of lymph node metastasis (LNM) in pancreatic neuroendocrine tumors (pNETs) are not well established. We sought to identify the value of MR imaging features in preoperatively predicting the lymph node metastasis of pNETs. MATERIALS AND METHODS: In this study, we enrolled 108 consecutive patients with pNETs between January 2009 and June 2018. MR morphologic features and quantitative data were evaluated. Predictors of LNM were evaluated using univariate and multivariate logistic regression models. RESULTS: A total of 108 patients with pNETs were finally enrolled, including 82 LNM-negative and 26 LNM-positive patients. Features significantly related to the LNM of pNETs at univariate analysis were tumor size > 2 cm (P = 0.003), Ki-67 > 5% (P = 0.002), non-enhancement pattern (P < 0.001), apparent diffusion coefficient value (P < 0.001), main pancreatic duct dilation (P < 0.001) and pancreatic atrophy (P = 0.032) and extrapancreatic tumor spread (P = 0.001), CNRs during arterial, portal and delay phase (P = 0.005, 0.047, and 0.045, respectively), and histological classification (P = 0.006). At multivariate analysis, non-enhancement pattern (P = 0.019; odds ratio, 6.652; 95% CI 1.369, 32.321) and main pancreatic duct dilation (P = 0.018; odds ratio, 6.745; 95% CI 1.379, 32.991) were independent risk factors for predicting the LNM of pNETs. CONCLUSION: The non-enhancement characteristic and main pancreatic duct dilation appear to be linked with LNM in pNETs. These radiological predictors can be easily obtained preoperatively, and may help to avoid missing pNETs with a high risk of LNM.
PURPOSES: Predictive factors of lymph node metastasis (LNM) in pancreatic neuroendocrine tumors (pNETs) are not well established. We sought to identify the value of MR imaging features in preoperatively predicting the lymph node metastasis of pNETs. MATERIALS AND METHODS: In this study, we enrolled 108 consecutive patients with pNETs between January 2009 and June 2018. MR morphologic features and quantitative data were evaluated. Predictors of LNM were evaluated using univariate and multivariate logistic regression models. RESULTS: A total of 108 patients with pNETs were finally enrolled, including 82 LNM-negative and 26 LNM-positive patients. Features significantly related to the LNM of pNETs at univariate analysis were tumor size > 2 cm (P = 0.003), Ki-67 > 5% (P = 0.002), non-enhancement pattern (P < 0.001), apparent diffusion coefficient value (P < 0.001), main pancreatic duct dilation (P < 0.001) and pancreatic atrophy (P = 0.032) and extrapancreatic tumor spread (P = 0.001), CNRs during arterial, portal and delay phase (P = 0.005, 0.047, and 0.045, respectively), and histological classification (P = 0.006). At multivariate analysis, non-enhancement pattern (P = 0.019; odds ratio, 6.652; 95% CI 1.369, 32.321) and main pancreatic duct dilation (P = 0.018; odds ratio, 6.745; 95% CI 1.379, 32.991) were independent risk factors for predicting the LNM of pNETs. CONCLUSION: The non-enhancement characteristic and main pancreatic duct dilation appear to be linked with LNM in pNETs. These radiological predictors can be easily obtained preoperatively, and may help to avoid missing pNETs with a high risk of LNM.
Authors: Jiake Xu; Jie Yang; Ye Feng; Jie Zhang; Yuqiao Zhang; Sha Chang; Jingqiang Jin; Xia Du Journal: Front Oncol Date: 2022-05-19 Impact factor: 5.738