A simple morphological classification to estimate the malignant potential of pancreatic neuroendocrine tumors
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A novel morphological classification using resected specimens predicted malignant potential and prognosis in patients with pancreatic neuroendocrine tumors (P-NETs). The aim of this study was to examine the predictive ability of morphological diagnoses made using non-invasive multi-detector computed tomography (MDCT) in P-NETs.
Between 2002 and 2015, 154 patients were diagnosed with P-NETs at the Tokyo Medical and Dental University, and 82 patients who underwent surgical treatment were enrolled. The primary tumors were classified by MDCT into three types: Type I, simple nodular tumor; Type II, simple nodular tumor with extra-nodular growth; and Type III, confluent multinodular tumor. Patients were stratified by 15 clinical specialists according to classification and without any other clinical or pathological information. Clinicopathological features and patient survival were reviewed retrospectively.
The mean observation time was 1004 days. Forty-six, 22, and 14 patients had Type I, II, and III tumors, respectively. Morphological classification was significantly correlated with advanced features such as tumor size, Ki-67 index, and synchronous liver metastasis (p < 0.001 for all). There were significant differences between all three tumor types as judged by ENETS TNM classification (p < 0.001), AJCC TNM classification (p = 0.046), WHO 2004 classification (p < 0.001), and WHO 2010 classification (p < 0.001). Five-year progression-free survival (PFS) rates for patients with Type I, II, and III tumors were 97, 43, and 31%, respectively (I vs. II, p < 0.001; I vs. III, p < 0.001; II vs. III, p = 0.017). Multivariate analysis revealed Type II/III tumors and synchronous liver metastasis to be independent risk factors for poor PFS.
A novel simple morphological classification system would predict Type II and III tumors that may have higher malignant potential than Type I tumors.
KeywordsPancreatic neuroendocrine tumors Morphological classification Multi-detector computed tomography WHO 2010 classification ENETS TNM classification AJCC TNM classification Single nodular with extra-nodular growth Confluent multinodular
This work was supported by Grant-in-Aid for Scientific Research (C) Grant Number 15K10046.
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Conflict of interest
The authors declare that they have no conflict of interest.
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