Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image Analysis.

ABSTRACT:

Background

The prognosis of patients with pancreatic neuroendocrine tumors (PanNET), the second most common type of pancreatic cancer, varies significantly, and up to 15% of patients develop metastasis. Although certain morphological characteristics of PanNETs have been associated with patient outcome, there are no available morphology-based prognostic markers. Given that current clinical histopathology markers are unable to identify high-risk PanNET patients, the development of accurate prognostic biomarkers is needed. Here, we describe a novel machine learning, multiclassification pipeline to predict the risk of metastasis using morphological information from whole tissue slides.

Methods

Digital images from surgically resected tissues from 89 PanNET patients were used. Pat

SUBMITTER: Klimov S 

PROVIDER: S-EPMC7946991 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature