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Updated: Nov 6, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in

Stefanie J Hectors1, Christine Chen1, Johnson Chen1

  • 1Department of Radiology, Weill Cornell Medicine, New York, New York, USA.

Journal of Magnetic Resonance Imaging : JMRI
|May 10, 2021
PubMed
Summary

Machine learning models using T2-weighted imaging radiomics can accurately identify clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions, outperforming traditional metrics like PSA density.

Keywords:
PI-RADSclinically significant prostate cancerprostate MRIradiomics

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions are equivocal, requiring further assessment for clinically significant prostate cancer (csPCa).
  • Accurate differentiation of csPCa in PI-RADS 3 lesions is crucial to guide biopsy decisions and avoid unnecessary procedures.

Purpose of the Study:

  • To develop and validate a machine learning model using radiomics features from T2-weighted imaging (T2 WI) to identify csPCa (Grade Group ≥ 2) in PI-RADS 3 lesions.
  • To compare the model's performance against prostate-specific antigen (PSA) density and prostate volume for csPCa prediction.

Main Methods:

  • A retrospective study included 240 patients with PI-RADS 3 lesions on multiparametric MRI.
  • Radiomics features (107) were extracted from T2 WI, and a random forest classifier was trained to predict csPCa.
  • Performance was evaluated using receiver operating characteristic (ROC) analysis.

Main Results:

  • The machine learning classifier achieved an area under the curve (AUC) of 0.76 (P=0.022) for csPCa prediction in the test set.
  • Prostate volume (AUC=0.62) and PSA density (AUC=0.61) showed moderate and non-significant performance.

Conclusions:

  • Machine learning models utilizing T2 WI radiomics offer a promising approach for predicting csPCa in PI-RADS 3 lesions.
  • This method demonstrates superior performance compared to traditional metrics, potentially improving diagnostic accuracy.