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Related Experiment Video

Updated: Jun 11, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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A Robust [18F]-PSMA-1007 Radiomics Ensemble Model for Prostate Cancer Risk Stratification.

Giovanni Pasini1,2, Alessandro Stefano3,4, Cristina Mantarro5

  • 1Institute of Bioimaging and Complex Biological Systems - National Research Council (IBSBC - CNR), Contrada, Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Journal of Imaging Informatics in Medicine
|September 30, 2024
PubMed
Summary

This study shows [18F]-PSMA-1007 PET radiomics can effectively differentiate high-risk from low-risk prostate cancer (PCa). An ensemble model achieved high accuracy, improving PCa risk stratification and reducing biopsy reliance.

Keywords:
Machine learningProstate cancerRadiomics[18F]-PSMA-1007 PET

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Area of Science:

  • Nuclear Medicine
  • Radiomics
  • Prostate Cancer Research

Background:

  • Prostate cancer (PCa) risk stratification is crucial for treatment decisions.
  • Biopsy-based methods for PCa risk assessment can be invasive and may not always be definitive.
  • Novel imaging biomarkers are needed to improve the accuracy of PCa risk stratification.

Purpose of the Study:

  • To investigate the efficacy of [18F]-PSMA-1007 PET radiomics in distinguishing high-risk from low-risk prostate cancer.
  • To develop and validate a robust radiomics ensemble model for PCa risk stratification.
  • To assess the potential of this approach to reduce reliance on traditional biopsy methods.

Main Methods:

  • Retrospective analysis of 143 prostate cancer patients who underwent [18F]-PSMA-1007 PET/CT imaging.
  • Extraction of 1781 IBSI-compliant radiomics features from PET images.
  • Development of a preliminary analysis pipeline with LASSO feature selection and cross-validation, followed by ensemble modeling using multiple feature subsets.

Main Results:

  • The radiomics ensemble model, particularly when trained with a combination of robust and fine-tuning features, achieved high performance metrics.
  • Highest average accuracy was 79.52%, AUC 85.75%, specificity 84.29%, precision 82.85%, and f-score 78.26%.
  • Statistically significant differences in performance metrics were observed across different ensemble models (p < 0.05).

Conclusions:

  • [18F]-PSMA-1007 PET radiomics shows significant potential for improving prostate cancer risk stratification.
  • The developed radiomics ensemble model offers a non-invasive method for differentiating high- and low-risk PCa.
  • This approach may reduce the need for invasive biopsies in PCa management.