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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Development and validation of an explainable artificial intelligence-based decision-supporting tool for prostate

Jungyo Suh1,2, Sangjun Yoo3, Juhyun Park4

  • 1Department of Urology, Hospital Medicine Center, Seoul National University Hospital, Seoul, South Korea.

BJU International
|May 27, 2020
PubMed
Summary

We developed an explainable AI tool to predict prostate cancer (PCa) and clinically significant PCa (csPCa) risk before biopsy. This tool accurately calculates the probability of PCa and csPCa, aiding clinical decisions.

Keywords:
decision-supporting toolexplainable AImachine learningprediction modelprostate cancerweb-based model

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Area of Science:

  • Artificial Intelligence in Medicine
  • Oncology
  • Medical Diagnostics

Background:

  • Prostate cancer (PCa) and clinically significant PCa (csPCa) detection often requires invasive prostate biopsies.
  • Accurate risk stratification prior to biopsy is crucial for optimizing patient management and reducing unnecessary procedures.
  • Explainable Artificial Intelligence (XAI) offers potential for developing transparent and interpretable diagnostic tools.

Purpose of the Study:

  • To develop and validate a risk calculator for predicting the probability of PCa and csPCa.
  • To utilize Explainable Artificial Intelligence (XAI) for enhanced transparency and interpretability of the risk prediction model.
  • To create a decision-support tool for clinicians to assess PCa and csPCa risk before prostate biopsy.

Main Methods:

  • Utilized a dataset of 3791 patients, divided into development (2843) and validation (948) sets.
  • Employed an extreme gradient-boosting algorithm with five-fold cross-validation and hyperparameter tuning for model development.
  • Determined model feature importance using Shapley values and selected variables via least absolute shrinkage and selection operator (LASSO) regression.

Main Results:

  • The developed risk calculator achieved an Area Under the Curve (AUC) of 0.869 for PCa and 0.945 for csPCa.
  • Key predictors for PCa included prostate-specific antigen (PSA), free PSA, age, prostate volume, hypoechoic lesions, and testosterone levels.
  • Previous biopsy history was negatively associated with PCa risk but not csPCa risk.

Conclusions:

  • Successfully developed and validated an XAI-based decision-support tool for pre-biopsy PCa and csPCa risk assessment.
  • The tool provides a probabilistic estimation of PCa and csPCa, aiding clinicians in diagnostic decision-making.
  • XAI enhances the interpretability of the risk calculator, fostering trust and clinical adoption.