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Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

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A modular cGAN classification framework: Application to colorectal tumor detection.

Thomas E Tavolara¹, M Khalid Khan Niazi², Vidya Arole³

¹Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, USA.

Scientific Reports

|December 14, 2019

Summary

This summary is machine-generated.

This study introduces a new framework for automatically identifying tumor regions in digital pathology slides, overcoming common challenges like reader variability and class imbalance for improved accuracy in cancer diagnosis.

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Area of Science:

Digital pathology
Computational pathology
Machine learning in histopathology

Background:

Automatic identification of tissue structures in digital biopsies is challenging due to reader variability, class imbalance, and model inflexibility.
Existing methods struggle with reliable ground truth and adapting to diverse tissue types.

Purpose of the Study:

To develop a framework for accurate automatic identification of tumor regions in colorectal cancer histopathology slides.
To overcome limitations of current digital pathology analysis methods using a novel approach.

Main Methods:

Utilized a framework based on conditional generative adversarial networks (cGANs).
Employed a minimally supervised, modular model-per-class paradigm.
Leveraged reliable immunohistochemistry ground truth for labeling and single-task learning to address class imbalances.

Main Results:

Achieved high performance metrics on validation and external test datasets for tumor region identification.
Validation set: Average precision (95.13%), sensitivity (93.05%), F1 score (94.02%).
External test set: Average precision (98.75%), sensitivity (88.53%), F1 score (93.31%).

Conclusions:

The developed framework demonstrates high accuracy in automatically identifying tumor regions in colorectal H&E slides.
Future work includes establishing a tumor front for tumor bud detection and integrating the model into a system for prognostic analysis.
This approach offers a robust solution for digital pathology analysis, paving the way for improved cancer diagnostics and prognostics.