ISUP Grade Prediction of Prostate Nodules on T2WI Acquisitions Using Clinical Features, Textural Parameters and
Teodora Telecan1,2, Alexandra Chiorean3, Roxana Sipos-Lascu3
1Department of Anatomy and Embryology, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.
Cancers
|June 26, 2025
Summary
This study developed an AI tool using MRI radiomics to differentiate aggressive prostate cancer from indolent types before biopsy. The model accurately predicts cancer grade, potentially reducing unnecessary invasive procedures.
Area of Science:
- Radiology
- Oncology
- Artificial Intelligence
Background:
- Prostate cancer (PCa) diagnosis requires differentiating clinically significant (csPCa) from indolent forms.
- Current methods rely on invasive prostate biopsies, as multi-parametric MRI (mpMRI) alone cannot distinguish between these categories.
- AI and radiomics offer a non-invasive approach to correlate medical imaging with pathology.
Purpose of the Study:
- To develop a machine learning model for differentiating indolent PCa from csPCa.
- To classify individual nodules into ISUP grades before prostate biopsy.
- To utilize textural features from mpMRI T2WI for improved diagnostic accuracy.
Main Methods:
- 154 patients with 201 prostatic lesions underwent 1.5 Tesla mpMRI.
- Nodules were delineated using 3D Slicer, and textural parameters were extracted with PyRadiomics.
- Three machine learning models (Random Forest, SVM, Logistic Regression) were compared.
Main Results:
- The Random Forest model achieved the highest performance in differentiating indolent from csPCa (88.13%) and ISUP 2 from ISUP 3 lesions (82.5%).
- Incorporating clinical data with radiomic signatures further improved accuracies to 91.11% and 91.39%, respectively.
- The model demonstrated strong predictive capability for ISUP grading.
Conclusions:
- An AI-driven decision support tool was developed for accurate pre-biopsy ISUP grading of prostate cancer.
- The tool leverages textural features from T2-weighted MRI acquisitions.
- This AI approach shows promise in improving prostate cancer diagnosis and management.
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