Searchable abstracts of presentations at key conferences in endocrinology

ea0098b9 | Basic Science | NANETS2023

Machine learning algorithm to classify multiphoton microscopy images of pancreatic neuroendocrine tumors

Daigle Noelle , Duan Suzann , Merchant Juanita L. , Sawyer Travis W.

Background: Surgery is the preferred method of treatment for most pancreatic neuroendocrine tumors (PNETs), particularly functional PNETs or those greater than 2 cm in largest dimension. Existing techniques include intraoperative ultrasound and manual palpation, both of which have inherent disadvantages such as poor resolution and low contrast against normal pancreatic tissue. This results in surgeons performing more demolitive resections, such as the Whipple procedure, when t...

ea0098b19 | Basic Science | NANETS2023

Label-free phenotyping of duodenal neuroendocrine tumors using tissue autofluorescence microscopy and digital spatial profiling

Knapp Thomas , Duan Suzann , Merchant Juanita , Sawyer Travis

Background: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are an extremely heterogenous group of diseases with complicated treatment and management decisions. For example, patients with Multiple Endocrine Neoplasia Type 1 (MEN1)-associated gastrinomas present with more aggressive tumors and poorer outcomes. Recent work has shown that sequencing (transcriptomic, proteomic) can phenotype GEP-NETs to accurately reflect important clinical parameters such as tumor aggress...

ea0098b1 | Basic Science | NANETS2023

Spatial profiling of neuro-immune interactions in gastroenteropancreatic NETs

Duan Suzann , Sawyer Travis W. , Witten Brandon L. , Song Heyu , Merchant Juanita L.

Background: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are heterogeneous malignancies that arise from complex cellular interactions within the tissue microenvironment. Until recently, the absence of reliable methods to unmix tumor- and stroma-derived signals has precluded a comprehensive understanding of how GEP-NETs arise in different tissues. Here, we sought to decipher tumor-derived signals from the surrounding microenvironment by applying Nanostring Digital Sp...