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  11. Prostate Cancer Risk Stratification By Digital Histopathology And Deep Learning

Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning

Yanan Shao1, Roozbeh Bazargani1, Davood Karimi2

  • 1Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.

JCO Clinical Cancer Informatics
|June 20, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning (ML) improves prostate cancer (PCa) risk stratification using histopathology images, outperforming traditional Gleason grading. This objective tool aids in treatment planning and potentially guides adjuvant therapy decisions for better patient outcomes.

Area of Science:

  • Oncology
  • Digital Pathology
  • Machine Learning

Background:

  • Prostate cancer (PCa) is heterogeneous, necessitating accurate risk assessment for treatment planning.
  • Current risk stratification relies on parameters like Gleason grading, which has significant interobserver variability.
  • Objective tools are needed to improve the accuracy of PCa risk stratification.

Purpose of the Study:

  • To determine if machine learning (ML)-driven histopathology image analysis can improve risk stratification for PCa.
  • To develop and evaluate a deep learning model combining clinicopathologic data with histopathology images for PCa risk assessment.

Main Methods:

  • A deep learning model was developed using hematoxylin and eosin- and Ki-67-stained histopathology images and clinicopathologic data.
  • The model was trained and tested on a dataset of 502 treatment-naïve PCa patients using five-fold cross-validation.

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  • Performance was evaluated using the concordance index, comparing it against Gleason grading and CAPRA-S models.

    • Main Results:

    • The ML-based convolutional neural network model demonstrated superior performance in risk stratification compared to Gleason grading and CAPRA-S.
    • The model correctly reclassified 3.9% of low-risk patients to high-risk and 21.3% of high-risk patients to low-risk.
    • This indicates improved accuracy in identifying patient risk categories.

    Conclusions:

    • ML-driven histopathology image analysis serves as an objective tool for PCa risk stratification.
    • The proposed digital pathology risk classification can aid in guiding adjuvant therapy decisions, such as radiotherapy, after radical prostatectomy.
    • Further validation on larger cohorts is recommended to solidify its clinical utility.