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Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens Publisher



Yazdani E1, 2 ; Geramifar P3 ; Karamzadeziarati N3 ; Sadeghi M1, 2 ; Amini P4 ; Rahmim A5, 6
Authors
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Authors Affiliations
  1. 1. Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, 14496-14535, Iran
  2. 2. Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran, 14496-14535, Iran
  3. 3. Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, 14117-13135, Iran
  4. 4. Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, 14496-14535, Iran
  5. 5. Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, V5Z 1L3, BC, Canada
  6. 6. Departments of Radiology and Physics, University of British Columbia, Vancouver, V5Z 1L3, BC, Canada

Source: Diagnostics Published:2024


Abstract

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed. © 2024 by the authors.
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