Danielle Guerrero

Jordan Redemann, et al, Journal of Pathology Informatics, 2020
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.

Antje Neeb, et al, European Urology, 2021
Researchers set out to evaluate the role of the ATM kinase in metastatic castration-resistant prostate cancer (mCRPC) with the long-term goal of improving molecular stratification in patients. HALO and HALO AI were used in the analysis of 800 ATM immunohistochemistry samples. Neeb et al detected ATM loss by IHC in 11% of their patient cohort which was associated with increased genomic instability but was not associated with a worse outcome. An in vitro model of ATM loss showed sensitivity to ATR and PARP inhibitors, which may be further investigated in future clinical trials of patients with ATM loss.