Comparing Deep Learning and Immunohistochemistry in Determining the Site of Origin for Well-Differentiated Neuroendocrine Tumors
Metastatic neuroendocrine tumors behave differently according to site of origin and it is important clinically to identify the primary site in order to identify an appropriate therapy. The site of origin in neuroendocrine tumors are challenging to identify based on H&E alone and can require an immunohistochemistry (IHC) panel. Redemann and colleagues evaluated the performance of HALO AI, a deep-learning convolutional neural network (CNN) on site of origin identification from a set of metastatic well-differentiated neuroendocrine tumors with known sites of origin and compared against IHC slides scored by pathologists. HALO AI was trained with H&E-stained tissue microarrays and was then evaluated against IHC analysis to identify pancreas/duodenum, ileum/jejunum/duodenum, colorectum/appendix, and lung. Results showed that HALO AI correctly identified the site of origin in 70% of cases and IHC correctly identified 76% of cases. As this was statistically insignificant, the authors conclude that a trained CNN can identify a site of origin from a well differentiated neuroendocrine tumor using morphology data alone with accuracy similar to that of IHC, the clinical gold standard.