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Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

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Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of

Christian Roest¹, Thomas C Kwee¹, Igle J de Jong²

¹Department of Radiology, University Medical Center Groningen, Groningen, the Netherlands.

Radiology. Imaging Cancer

|January 10, 2025

Summary

This study developed a deep learning model using MRI and clinical data to predict prostate cancer progression. The model accurately identifies low-risk patients, aiding personalized follow-up strategies.

Keywords:

Deep Learning MRI Prostate Cancer

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Area of Science:

Radiology
Oncology
Artificial Intelligence

Background:

Prostate cancer (PCa) progression risk stratification is crucial for patient management.
Accurate prediction models are needed to personalize follow-up strategies and avoid unnecessary interventions.

Purpose of the Study:

To validate a deep learning (DL) model for predicting PCa progression risk using MRI and clinical data.
To compare the DL model's performance against established risk calculators.

Main Methods:

A retrospective study of 1143 patients with suspected clinically significant PCa (csPCa) using 1607 MRI scans.
A DL model was developed incorporating baseline MRI and clinical parameters (age, PSA, PSA density, prostate volume).
Internal and external validation was performed, comparing predictive performance with ERSPC and PCPT risk calculators using Harrell C-index.

Main Results:

The DL model significantly predicted csPCa progression in both internal (HR, 1.97) and external (HR, 1.32) cohorts.
It identified a subgroup of patients (approx. 20%) with very low 1-, 2-, and 4-year progression risks (≤3%, ≤8%, ≤18%).
The DL model demonstrated superior predictive performance (C-index 0.68 internal, 0.56 external) compared to ERSPC and PCPT at internal testing.

Conclusions:

The validated DL model accurately predicts PCa progression risk.
It offers improved risk stratification, enabling personalized follow-up for low-risk PCa patients.
This AI-driven approach has the potential to optimize patient management and reduce healthcare burdens.