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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale

Ana Carolina Rodrigues1,2, José Guilherme de Almeida1, Nuno Rodrigues1,3

  • 1Champalimaud Research, Champalimaud Foundation, Computational Clinical Imaging, Av. Brasília, Doca de Pedrouços, Lisboa, Lisbon, PT 1400-038, Portugal.

Radiology. Imaging Cancer
|August 15, 2025
PubMed
Summary
This summary is machine-generated.

A new multimodal model using biparametric MRI (bpMRI) radiomics accurately predicts clinically significant prostate cancer (csPCa), outperforming PI-RADS and reducing unnecessary biopsies. This advanced algorithm offers improved diagnostic accuracy for oncology.

Keywords:
Algorithm DevelopmentComparative StudiesGenital/ReproductiveMachine LearningModel TrainingModel ValidationNeoplasms-PrimaryOncologyTechnology Assessment

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Area of Science:

  • Oncology and Genitourinary Medicine
  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Clinically significant prostate cancer (csPCa) detection relies on accurate imaging and risk stratification.
  • Biparametric MRI (bpMRI) is increasingly used, but its predictive performance can be enhanced.
  • Existing models like PI-RADS have limitations in specificity and sensitivity.

Purpose of the Study:

  • To develop and prospectively validate a novel clinical and radiologic model for predicting csPCa.
  • To integrate bpMRI radiomics with clinical data for improved prediction accuracy.
  • To compare the performance of the new multimodal model against the PI-RADS scoring system.

Main Methods:

  • A deep learning algorithm extracted radiomic features from bpMRI scans of 7157 patients (development) and 1629 patients (validation).
  • A multimodal model was trained incorporating radiomics, age, prostate-specific antigen, PI-RADS, and lesion location.
  • Prospective validation assessed the model's performance using AUC and specificity, with sensitivity analyses for imaging sequences and scanner vendors.

Main Results:

  • The multimodal model demonstrated superior performance over PI-RADS in both retrospective (AUC 0.88 vs 0.80) and prospective (AUC 0.91 vs 0.85) validation.
  • The model achieved higher specificity (71% vs 58% retrospective, 77% vs 66% prospective) and led to 22.7% fewer biopsies.
  • Fairness analyses indicated generalizability across categories, though performance varied slightly across centers and with PI-RADS scores.

Conclusions:

  • A multimodal model integrating bpMRI radiomics offers a temporally generalizable and superior predictor of csPCa compared to PI-RADS.
  • This approach has the potential to significantly improve prostate cancer diagnostics and reduce invasive procedures.
  • Further validation and implementation could enhance clinical decision-making in prostate cancer management.