Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy.

Journal: Biomedical engineering online
Published Date: Dec 23, 2024

Abstract

PURPOSE: This study aims to accurately predict the effects of hormonal therapy on prostate cancer (PC) lesions by integrating multi-modality magnetic resonance imaging (MRI) and the clinical marker prostate-specific antigen (PSA). It addresses the limitations of Convolutional Neural Networks (CNNs) in capturing long-range spatial relations and the Vision Transformer (ViT)'s deficiency in localization information due to consecutive downsampling. The research question focuses on improving PC response prediction accuracy by combining both approaches.

Authors

  • Ibrahim Abdelhalim
    Department of Bioengineering, University of Louisville, Louisville, KY, USA.
  • Mohamed Ali Badawy
    Radiology Department, Urology and Nephrology Center, Mansoura, Egypt.
  • Mohamed Abou El-Ghar
    4 Radiology Department, Mansoura University, Mansoura, Egypt.
  • Mohammed Ghazal
    3 Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
  • Sohail Contractor
    Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
  • Eric van Bogaert
    Department of Radiology, University of Louisville, Louisville, KY, USA.
  • Dibson Gondim
    Department of Pathology and Laboratory Medicine, University of Louisville, Louisville, KY, USA.
  • Scott Silva
    Department of Radiation Oncology, University of Louisville, Louisville, KY, USA.
  • Fahmi Khalifa
    Bioengineering Department, University of Louisville, Louisville, KY, USA.
  • Ayman El-Baz
    Bioengineering Department, The University of Louisville, Louisville, KY, USA.

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