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Author Notes:

sklimov3@gmail.com; michelle.reid@emory.edu; raneja@gsu.edu

SK and MR: conception and design. SK and AG: data analysis, image analysis, statistical analysis, and manuscript writing. YX, SB, SP, and RG: collection of data and materials. YX and MR: case screening and annotation. All authors: data interpretation, critical manuscript review and revision, and approval of the final manuscript. All authors contributed to the article and approved the submitted version.

SK thankfully acknowledges the fellowship from MBD (Molecular Basis of Disease).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Subjects:

Research Funding:

This study was supported by a grant from the National Cancer Institute (U01 CA179671) to RA.

Keywords:

  • metastasis risk assessment
  • deep learning
  • histological image analysis
  • pancreatic neuroendocrine tumors
  • computational pathology

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

Journal Title:

Frontiers in Oncology

Volume:

Volume 10

Publisher:

Type of Work:

Article | Final Publisher PDF

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.

Copyright information:

© 2021 Klimov, Xue, Gertych, Graham, Jiang, Bhattarai, Pandol, Rakha, Reid and Aneja

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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