Hotspot detection in pancreatic neuroendocrine images using local depth

There is a recent and increasing trend in the incidence of pancreatic neuroendocrine tumors (PNETs). Ki-67 proliferative index is required for routine pathologic evaluation of PNETs. This index has been found to be a consistent prognostic factor to assess clinical/prognostic outcome of PNETs. Unfortunately, we still lack a standardized method to reliably obtain the Ki-67 proliferative index. As part of a large study to standardize this index, here we present an accurate, easy-to- use, reproducible method to identify tumor nuclei and hotspots within PNETs. We modified the U-Net image segmentation architecture to identify tumor positive and negative nuclei. We also introduced the concept of local depth for identification of hotspots. On an independent test set of 8 whole slide images, the modified U-Net achieved a sensitivity of 96.2% and specificity of 93.3%. The hotspot detection framework resulted in a dice coefficient of 0.81. The method has the potential to not only facilitate the detection of tumor nuclei, but can be adapted to reproduce hotspots by pathologists.