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Automating Prostate Cancer Grading: A Novel Deep Learning Framework for Automatic Prostate Cancer Grade Assessment

Saidul Kabir1, Rusab Sarmun1, Rafif Mahmood Al Saady2

  • 1Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.

Journal of Imaging Informatics in Medicine
|February 6, 2025
PubMed
Summary

This study introduces an automated deep learning system to improve prostate cancer (PCa) grading accuracy. The innovative framework enhances diagnostic precision, offering a reliable tool for assessing PCa severity and aiding clinical decisions.

Keywords:
Artificial intelligenceClassificationDeep learningISUP gradingProstate cancerSegmentation

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Prostate cancer (PCa) grading relies on the subjective Gleason system, necessitating more objective methods.
  • Current diagnostic variability highlights the need for automated, reliable tools to improve accuracy and minimize human error.
  • Deep learning offers potential for enhanced precision in medical image analysis and cancer grading.

Purpose of the Study:

  • To develop and evaluate an innovative three-stage deep learning framework for automated prostate cancer grading.
  • To improve the accuracy and reliability of PCa severity assessment using automated systems.
  • To provide a prospective prognostic tool for clinically significant and efficient PCa evaluation.

Main Methods:

  • A three-stage deep learning framework was developed using the PANDA challenge dataset (2699 cases).
  • Stages included: classification of PCa grades (benign, Gleason 3-5) using DNNs, segmentation of PCa grades, and ISUP grade computation via ML classifiers.
  • Optimized patch sampling, DeepLabV3 with Self-ONN, and EfficientNet_b0 were employed for classification and segmentation.

Main Results:

  • EfficientNet_b0 achieved an 83.83% F1-score for classification.
  • DeepLabV3+ with Self-ONN and EfficientNet encoder yielded an 84.9% Dice Similarity Coefficient (DSC) for segmentation.
  • The framework achieved a 0.9215 quadratic weighted kappa (QWK) score using a RandomForest classifier for final ISUP grade prediction.

Conclusions:

  • The proposed deep learning framework demonstrates promising results for automated prostate cancer grading.
  • The system offers a reliable and efficient approach for assessing PCa severity, potentially serving as a prognostic tool.
  • Further research is needed to validate the framework's adaptability across diverse clinical settings.