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External Validation of an Artificial Intelligence Algorithm Using Biparametric MRI and Its Simulated Integration with

Mason J Belue1, Vaneeza Mukhtar1, Roopa Ram2

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Summary

Artificial Intelligence (AI) demonstrated comparable prostate cancer (PCa) detection to PI-RADS. Combining AI with radiologist interpretation improved sensitivity for detecting clinically significant PCa, especially for PI-RADS 3 lesions.

Keywords:
Artificial intelligenceMachine learningMagnetic resonance imagingProstate biopsyProstate cancer

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate imaging reporting and data systems (PI-RADS) exhibits significant inter-reader variability in performance.
  • Artificial Intelligence (AI) algorithms show promise for assessing prostate cancer (PCa) risk, but require validation in clinical settings.

Purpose of the Study:

  • To evaluate an AI algorithm for PCa detection in a real-world clinical practice.
  • To simulate the integration of an AI model with PI-RADS for evaluating indeterminate PI-RADS 3 lesions.

Main Methods:

  • Retrospective external validation of a biparametric MRI-based AI model for PCa detection.
  • Comparison of AI predictions with biopsy results in 144 patients undergoing prostate MRI and biopsy.
  • Simulation of AI integration to adjust biopsy thresholds for PI-RADS category 3 lesions.

Main Results:

  • AI sensitivity for PCa (86.6%) and clinically significant PCa (csPCa, 88.4%) was comparable to radiologists (85.7% and 89.5%).
  • Combining radiologist and AI evaluations improved csPCa sensitivity by 5.8% (p=0.025).
  • The combination of AI, PI-RADS, and PSA density yielded the best diagnostic performance for csPCa (AUC=0.76).

Conclusions:

  • The AI algorithm achieved PCa detection rates similar to PI-RADS.
  • Integrating AI with radiologist interpretation enhances sensitivity, particularly for PI-RADS 3 lesions.
  • Further research is needed to define AI's role in PCa screening.