Publication

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

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Last modified
  • 05/15/2025
Type of Material
Authors
    Sergey Klimov, Georgia State UniversityYue Xue, Emory UniversityArkadiusz Gertych, Cedars-Sinai Medical CenterRondell P. Graham, Mayo ClinicYi Jiang, Emory UniversityShristi Bhattarai, Georgia State UniversityStephen J. Pandol, Cedars-Sinai Medical CenterEmad A. Rakha, University of NottinghamMichelle Reid, Emory UniversityRitu Aneja, Georgia State University
Language
  • English
Date
  • 2021-02-25
Publisher
  • Frontiers Media
Publication Version
Copyright Statement
  • © 2021 Klimov, Xue, Gertych, Graham, Jiang, Bhattarai, Pandol, Rakha, Reid and Aneja
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 10
Grant/Funding Information
  • This study was supported by a grant from the National Cancer Institute (U01 CA179671) to RA.
Supplemental Material (URL)
Abstract
  • 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.
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Keywords
Research Categories
  • Biology, Neuroscience
  • Health Sciences, Oncology
  • Health Sciences, Pathology

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