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Updated: Jun 27, 2025
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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
170
PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI
Riccardo Laudicella1,2,3, Albert Comelli4, Moritz Schwyzer5
1Department of Nuclear Medicine, University Hospital Zürich, University of Zurich, Zurich, Switzerland. riclaudi@hotmail.it.
La Radiologia Medica
|May 3, 2024
Summary
This study shows that artificial intelligence can predict prostate-specific membrane antigen (PSMA) levels using only T2 MRI scans. This deep learning approach may help identify aggressive prostate cancer (PCa) without needing PET imaging.
Area of Science:
- Radiology
- Artificial Intelligence
- Oncology
Background:
- Prostate-specific membrane antigen (PSMA) expression in prostate cancer (PCa) may correlate with histopathological features not visible on standard MRI.
- Deep learning analysis of structural MRI could potentially predict PSMA positivity.
- Predicting PSMA uptake from MRI alone could enhance PCa staging and treatment planning.
Purpose of the Study:
- To train a neural network using PSMA PET/MRI scans to predict increased prostatic PSMA uptake solely from axial T2-weighted MRI sequences.
- To assess the feasibility of deep structural image analysis for predicting PSMA expression in PCa.
- To develop a predictive model for PSMA uptake based on MRI findings.
Main Methods:
- A cohort of 154 patients undergoing simultaneous PSMA PET/MRI for PCa staging or biopsy guidance was analyzed.
- Prostatic beds on PSMA PET scans were dichotomized into positive and negative regions using an SUV threshold > 4 to create a PSMA PET map.
- A Convolutional Encoder-Decoder Network (C-ENet) was trained on T2-weighted MRI images to generate a predictive PSMA PET map.
Main Results:
- The study included 154 PSMA PET/MRI scans, with significant cancer detected in 127 patients.
- The dataset was split into a training cohort (n=124) and a test cohort (n=30).
- The C-ENet model achieved a Dice similarity coefficient of 69.5% ± 15.6% in predicting the PSMA PET map from T2 MRI.
Conclusions:
- Prostatic PSMA uptake on PET imaging may be predictable using only T2-weighted MRI.
- This AI-driven approach shows potential for estimating PSMA uptake, aiding mpMRI interpretation.
- Further validation with larger cohorts is necessary to confirm the accuracy and clinical utility of this predictive method.
