Yazdani, E.; Geramifar, P.; Karamzade-Ziarati, N.; Sadeghi, M.; Amini, P.; Rahmim, A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics2024, 14, 181.
Yazdani, E.; Geramifar, P.; Karamzade-Ziarati, N.; Sadeghi, M.; Amini, P.; Rahmim, A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics 2024, 14, 181.
Yazdani, E.; Geramifar, P.; Karamzade-Ziarati, N.; Sadeghi, M.; Amini, P.; Rahmim, A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics2024, 14, 181.
Yazdani, E.; Geramifar, P.; Karamzade-Ziarati, N.; Sadeghi, M.; Amini, P.; Rahmim, A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics 2024, 14, 181.
Abstract
Radiotheranostics refers to pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radio-pharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiother-apeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly 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 radio-theranostics, focusing on pairs of SSTR- or PSMA‑targeting radioligands, describing the funda-mental concepts and specific imaging/treatment features. Our review includes ligands radio-labeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions by radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restag-ing, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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