Comparing Deep Learning and Immunohistochemistry in Determining the Site of Origin for Well-Differentiated Neuroendocrine Tumors

Authors

Jordan Redemann, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM, USA
Fred A. Schultz, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM, USA
Cathy Martinez, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM, USA
Michael Harrell, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM, USA
Douglas P. Clark, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM, USA
David R. Martin, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM, USA
Joshua A. Hanson, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM, USA

Document Type

Article

Publication Date

10-1-2020

Abstract

Background: Determining the site of origin for metastatic well-differentiated neuroendocrine tumors (WDNETs) is challenging, and immunohistochemical (IHC) profiles do not always lead to a definitive diagnosis. We sought to determine if a deep-learning convolutional neural network (CNN) could improve upon established IHC profiles in predicting the site of origin in a cohort of WDNETs from the common primary sites.

Materials and methods: Hematoxylin and eosin (H&E)-stained tissue microarrays (TMAs) were created using 215 WDNETs arising from the known primary sites. A CNN trained and tested on 60% (n = 130) and 40% (n = 85) of these cases, respectively. One hundred and seventy-nine cases had TMA tissue remaining for the IHC analysis. These cases were stained with IHC markers pPAX8, CDX2, SATB2, and thyroid transcription factor-1 (markers of pancreas/duodenum, ileum/jejunum/duodenum, colorectum/appendix, and lung WDNET sites of origin, respectively). The CNN diagnosis was deemed correct if it designated a majority or plurality of the tumor area as the known site of origin. The IHC diagnosis was deemed correct if the most specific marker for a particular site of origin met an H-score threshold determined by two pathologists.

Results: When all cases were considered, the CNN correctly identified the site of origin at a lower rate compared to IHC (72% vs. 82%, respectively). Of the 85 cases in the CNN test set, 66 had sufficient TMA material for IHC stains, thus 66 cases were available for a direct case-by-case comparison of IHC versus CNN. The CNN correctly identified 70% of these cases, while IHC correctly identified 76%, a finding that was not statistically significant (P = 0.56).

Conclusion: A CNN can identify WDNET site of origin at an accuracy rate close to the current gold standard IHC methods.

Recommended Citation

Redemann J, Schultz FA, Martinez C, Harrell M, Clark DP, Martin DR, Hanson JA. Comparing Deep Learning and Immunohistochemistry in Determining the Site of Origin for Well-Differentiated Neuroendocrine Tumors. J Pathol Inform. 2020 Oct 9;11:32. doi: 10.4103/jpi.jpi_37_20. PMID: 33343993; PMCID: PMC7737494.