Multimodality imaging in prostate cancer diagnosis using artificial intelligence: basic concepts and current state-of-the-art

Abstract The early diagnosis of prostate cancer (PCa) is highly recommended, as the tumor will not spread to other organs of the body and the bones. Moreover, a late diagnosis of PCa could lower the survival rate. The growing development of Artificial Intelligence (AI) and Machine Learning (ML) in medical images has led to significant improvement in PCa diagnosis. Multimodality is now commonly applied in medical imaging diagnosis, as it provides comprehensive information about a target (tissue or tumor). It has shown to be useful for advancing the clinical reliability of using medical images and ML for medical diagnostics and analysis. Hence, in this paper, a comprehensive survey is provided to explore the state-of-the-art Computer-Aided Diagnosis Systems (CADs) for PCa detection attributed to multimodality imaging, a background of PCa. different types of medical imaging used in PCa diagnosis, related clinical workflows, future perspectives, and some common limitations of related work. The review exhibits an extensive literature review done on multimodality imaging in PCa, highlights that multimodality imaging has the potential of wide applicability in diagnosis systems. It is expected that this study enhances the understanding necessary for developing CAD systems for PCa diagnosis. Additionally, it is expected to establish a great basis for developing multimodal images, the relevant datasets, some of the challenges, and future topics. Graphical Abstract