PNET recurrence risk prediction model based on basic clinicopathological information and VISTA testing

Abstract

The invention provides a model based on clinical staging and functional status, immune checkpoints VISTA, PD-L1 and microvascular density that can be used for predicting the recurrence risk of G1 and G2 pancreatic neuroendocrine tumors. According to clinical stages and functional status, detection of PD-L1, VISTA, and CD34 by immunohistochemistry, determination of immunohistochemistry results by microscopy, a column diagram is made synthesizing the above five variables, risk of recurrence of pancreatic neuroendocrine tumor at 1, 3, and 5 years after surgery operation is predicted according to the column diagram. The model can accurately predict the recurrence risk of the G1 and G2 pancreatic neuroendocrine tumors, thus providing a reference for the development of individualized follow-up protocols. The model is jointly established based on clinical pathology, tumor immune microenvironment and microvessels, and has operability and comprehensiveness.

Description

technical field [0001] The invention relates to the field of biomedicine, in particular to a PNET recurrence risk prediction model based on basic clinicopathological information and VISTA detection. Background technique [0002] Pancreatic neuroendocrine tumors (PNET) are a group of tumors with neuroendocrine differentiation that have large heterogeneity in clinical manifestations, molecular changes, and prognosis. According to the statistics of the Surveillance, Epidemiology and End Results (SEER) project, its annual incidence rate is about 0.48 per 100,000 people, and it is still increasing year by year [1] . For pancreatic neuroendocrine tumors, the most commonly used clinical and pathological prognosis evaluation method is the pancreatic neuroendocrine tumor grading system formulated by the World Health Organization (WHO). Below, each method will be briefly described. [0003] The grading system for pancreatic neuroendocrine tumors developed by WHO, which is derived fr...

AI Technical Summary

Problems solved by technology

Some individuals are prone to recurrence and metastasis after surgery, while others have no signs of metastasis and recurrence after long-term follow-up

It shows that the existing grading system cannot distinguish the population according to the risk of recurrence, and it is difficult to consider the interval and method of follow-up in a targeted manner.

In addition, this system only considers tumor differentiation and proliferation, while ignoring the influence of tumor microenvironment and other clinical and pathological features on tumor recurrence.

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