Histological grades and prognostic markers of well-differentiated pancreatic neuroendocrine tumor (W

来源 :胰腺病学杂志(英文) | 被引量 : 0次 | 上传用户:dionysos223
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Pancreatic neuroendocrine tumors (PNETs) are the second most common neoplasm of the pancreas after ductal adenocarcinoma. The clinical behavior of PNETs is very difficult to predict, especially for well-differentiated PNETs. Several classification systems have been developed to differentiate benign from malignant PNETs. The current 5th edition WHO (2019) updated the grading system of PNETs which is now entirely based on proliferative rate (either mitotic count or Ki-67 index). In this article, we systemically reviewed the evolution of the histologic classification and grading systems for well-differentiated PNETs. Meanwhile, we also report a comprehensive summary of immunohistochemical and molecular prognostic factors for PNETs patients, including CK19, KIT, PDL1/PD-L1, UCH-L1 and CNPY2. We believe that more reliable grading systems and prognostic markers are emerging and will change the landscape of treatment for well-differentiated PNETs.“,”Pancreatic neuroendocrine tumors (PNETs) are the second most common neoplasm of the pancreas after ductal adenocarcinoma. The clinical behavior of PNETs is very difficult to predict, especially for well-differentiated PNETs. Several classification systems have been developed to differentiate benign from malignant PNETs. The current 5th edition WHO (2019) updated the grading system of PNETs which is now entirely based on proliferative rate (either mitotic count or Ki-67 index). In this article, we systemically reviewed the evolution of the histologic classification and grading systems for well-differentiated PNETs. Meanwhile, we also report a comprehensive summary of immunohistochemical and molecular prognostic factors for PNETs patients, including CK19, KIT, PDL1/PD-L1, UCH-L1 and CNPY2. We believe that more reliable grading systems and prognostic markers are emerging and will change the landscape of treatment for well-differentiated PNETs.
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