PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.

Journal: La Radiologia medica
PMID: 38700556

Abstract

PURPOSE: High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and therefore predict if a lesion would be PSMA positive. Therefore, we aimed to train a neural network based on PSMA PET/MRI scans to predict increased prostatic PSMA uptake based on the axial T2-weighted sequence alone.

Authors

  • Riccardo Laudicella
    Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, Italy.
  • Albert Comelli
    Ri.MED Foundation, Palermo, Italy.
  • Moritz Schwyzer
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland.
  • Alessandro Stefano
    Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy.
  • Ender Konukoglu
  • Michael Messerli
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland. Electronic address: michael.messerli@usz.ch.
  • Sergio Baldari
    Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, Italy.
  • Daniel Eberli
    Department of Urology University Hospital Zurich Zurich Switzerland.
  • Irene A Burger
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland.

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