Deep learning-based automated hot-spot detection and tumor grading in human gastrointestinal neuroendocrine tumor

Darshana Govind, Kuang Yu Jen, Pinaki Sarder

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ki-67 index is an important diagnostic factor in gastrointestinal neuroendocrine tumor (GI-NET). The current gold standard for grading GI-NETs involves the visual screening of histopathologically stained tissues, for hot-spots containing high amounts of proliferating tumor cells (stained with Ki-67 antibody). Subsequently, the Ki-67 index, i.e. the percentage of proliferating tumor cells within the hot-spot is manually obtained. To automate this subjective and time consuming process, we have developed an integrated pipeline, termed SKIE (synaptophysin-Ki-67 index estimator), combining double-immunohistochemical (IHC) staining for synaptophysin (stains tumor) and Ki-67, with whole slide image (WSI) analysis. The Ki-67 index for 50 human GI-NET WSIs were estimated by SKIE and compared with three pathologists' assessment, and the gold standard (exhaustive counting by a fourth pathologist) based on the double-stained image. All four pathologists unanimously graded 38 WSIs, among which, SKIE achieved 94.74% accuracy. One discrepant case was attributed to staining inconsistencies and the other to SKIE selecting a better hot-spot. The remaining 12 WSIs had discrepant grades among pathologists, and hence, the gold standard was chosen for comparison, wherein, 10 WSI grades matched with that of the gold standard, and SKIE assigned a lower and higher grade to two cases. Overall, SKIE agreed with the gold standard with a substantial linear weighted Cohen's kappa κ = 0.622 with CI [0.286, 0.958]. We further expanded our method to deep-SKIE, wherein, a deep convolutional neural network (DCNN) was trained and validated using 13,736 hotspot-sized tiles from 40 WSIs, each categorized into one of four classes (background, non-tumor, tumor grade 1, tumor grade 2) by SKIE and tested on 9 WSIs. Deep-SKIE achieved an accuracy of 91.63% with near-perfect agreement (κ = 0.88 with CI [0.87, 0.89]) with the gold standard.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
PublisherSPIE
ISBN (Electronic)9781510634077
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Digital Pathology - Houston, United States
Duration: Feb 19 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11320
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Digital Pathology
Country/TerritoryUnited States
CityHouston
Period2/19/202/20/20

Keywords

  • Deep-SKIE
  • Gastrointestinal neuroendocrine tumor (GI-NETs)
  • SKIE
  • Tumor grading
  • Whole slide image analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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