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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens.

Kamal Hammouda1, Fahmi Khalifa1, Moumen El-Melegy2

  • 1BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.

Sensors (Basel, Switzerland)
|October 26, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a computer-aided diagnostic (CAD) system for prostate cancer grading using deep learning on digitized biopsy specimens. The system accurately classifies Gleason patterns and grade groups, improving diagnostic efficiency.

Keywords:
CAD systemclassificationdeep learninggrade groupsprostate cancer

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Area of Science:

  • Digital pathology
  • Medical imaging analysis
  • Computational oncology

Background:

  • Prostate cancer is a leading cause of cancer morbidity and mortality in the USA.
  • Accurate grading of prostate biopsy specimens is crucial for treatment decisions.
  • Existing diagnostic methods can be time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnostic (CAD) system for automated Gleason pattern and grade group classification.
  • To utilize a pyramidal deep learning approach for enhanced classification accuracy.
  • To improve the efficiency and objectivity of prostate cancer grading.

Main Methods:

  • Development of a pyramidal deep learning system using three Convolutional Neural Networks (CNNs) for patch- and pixel-wise classification.
  • Sequential preprocessing including histogram equalization and edge enhancement of digitized prostate biopsy specimens (PBSs).
  • Majority voting technique applied for pixel-wise classification and final image generation, trained and validated on 608 whole slide images (WSIs).

Main Results:

  • The CNNL model achieved the highest patch classification accuracy at 0.76, with macro- and weighted-averaged metrics between 0.70-0.77.
  • The CAD system demonstrated strong performance for grade group classification with ~80% precision, 60-80% recall/F1-score, and 94% accuracy/NPV.
  • Comparative analysis showed competitive results against standard ResNet50, VGG16, and previous work.

Conclusions:

  • The proposed CAD system effectively automates Gleason pattern and grade group classification from digitized prostate biopsy specimens.
  • The pyramidal deep learning approach enhances the extraction of both local and global features for improved diagnostic accuracy.
  • This automated system holds significant potential for improving the efficiency and consistency of prostate cancer diagnosis.