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Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging.

Shachi Mittal1,2, Jonathan Kim3, Rohit Bhargava1,4,5

  • 197472Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

Applied Spectroscopy
|March 17, 2022
PubMed
Summary

Infrared (IR) spectroscopic imaging offers new ways to analyze histopathology. This study develops methods for sample size estimation and automated annotation transfer, enabling large-scale validation studies using spectral data.

Keywords:
IR spectroscopic imagingInfraredclusteringdigital annotationsimage registrationmultivariate analysis of variancepower analysis

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Area of Science:

  • Biomedical Engineering
  • Computational Pathology
  • Spectroscopic Imaging

Background:

  • Infrared (IR) spectroscopic imaging advancements enable new approaches for histopathology analysis.
  • Large-scale validation studies are crucial for spectral histopathology but face challenges with data and annotations.

Purpose of the Study:

  • To assess the potential of IR metrics for discriminating histologic classes.
  • To estimate sample sizes for validation studies.
  • To develop an automated tool for transferring pathologist annotations from stained images to IR images.
  • To establish a scheme for identifying diagnostic groups and pure chemical pixels for training histopathological models.

Main Methods:

  • Examined the discrimination potential of IR metrics for various histologic classes.
  • Developed an automated annotation transfer tool for large-scale training/validation.
  • Applied a combination of supervised and unsupervised analysis to identify diagnostic patterns.
  • Isolated pure chemical pixels for each class to enhance model training.

Main Results:

  • IR metrics show potential for discriminating between histologic classes.
  • The automated annotation transfer tool effectively overcomes limitations of sparse ground truth data.
  • The combined analysis successfully identified diagnostic groups and patterns.
  • Pure chemical pixels were isolated, improving the training of complex histopathological models.

Conclusions:

  • The developed methods provide essential tools for utilizing large spectroscopic imaging datasets in histopathology.
  • Automated annotation transfer and robust analysis schemes facilitate large-scale validation studies.
  • This work advances the integration of spectral data into diagnostic pathology.