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Related Experiment Video

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images.

Amir Hadjifaradji1, Michael Diaz-Stewart1, Jenny Chu2,3

  • 1School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 2B9, Canada.

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A new machine learning model accurately grades neuroendocrine neoplasms (NENs) using H&E and Ki-67 stains. It also identified a unique subset of grade 1 NENs with distinct survival outcomes, potentially improving NEN prognostication.

Keywords:
deep learninggradingmitotic figuresneuroendocrineobject detection

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Digital pathology for NEN grading

Background:

  • Neuroendocrine neoplasms (NENs) are rare tumors requiring accurate grading for prognosis and treatment.
  • Current grading relies on mitotic count and Ki-67 index, which suffer from inter- and intra-observer variability.
  • Pathologist quantification of proliferation markers is time-consuming and prone to errors.

Purpose of the Study:

  • To develop and validate a machine learning (ML) pipeline for automated NEN grading.
  • To assess the performance of ML models using Hematoxylin and Eosin (H&E) and Ki-67 stains.
  • To investigate the prognostic significance of ML-identified NEN grades, particularly for grade 1 tumors.

Main Methods:

  • A dataset of 385 gastroenteropancreatic NEN samples (186 patients) with H&E and Ki-67 images was used.
  • Three ML frameworks were evaluated: H&E only, H&E with Ki-67, and pathologist-corrected cases.
  • Model performance was assessed using balanced accuracy for grading and survival analysis (c-index).

Main Results:

  • The H&E-based ML framework achieved 77.5% balanced accuracy in NEN grading.
  • Incorporating Ki-67 images improved grading accuracy to 83.0%.
  • AI-identified grade 1 NENs with higher assigned grades showed significantly shorter median survival (4.22 years) compared to concordant grade 1 NENs (10.13 years).

Conclusions:

  • The developed ML model accurately grades NENs, offering a potential solution to current grading challenges.
  • The model identified a prognostically distinct subgroup of grade 1 NENs, suggesting limitations in current grading systems.
  • Further research is warranted to validate these findings and explore the clinical entity of this discordant group.