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Review
. 2024 Jan 14;14(2):181.
doi: 10.3390/diagnostics14020181.

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

Elmira Yazdani  1   2 , Parham Geramifar  3 , Najme Karamzade-Ziarati  3 , Mahdi Sadeghi  1   2 , Payam Amini  4 , Arman Rahmim  5   6
Affiliations
Review

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

Elmira Yazdani et al. Diagnostics (Basel). .

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.

Keywords: PSMA; SSTR; artificial intelligence; personalized dosimetry; radiomics; radiotheranostics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The principle of radiotheranostics in mCRPC patients. (A). The typical timeline of different therapies, including RPT (also known as RLT). (B). PSMA-binding domain, linker, and chelator labeled with Lu-177 deliver ionizing radiation to the tumor.
Figure 2
Figure 2
Schematic overview of radiotheranostics principle in NETs patients and the development of radiopharmaceutical. The chelator labeled with Lu-177 binds to SSTRs and delivers ionizing radiations to destroy tumor cells.
Figure 3
Figure 3
Radiomics and AI workflow from image acquisition to radiomics modeling.
Figure 4
Figure 4
A broad range of AI applications in a chain from radiochemistry to a physician’s report generation for a prostate cancer patient who underwent [68Ga]Ga-PSMA-11 PET/CT scan based on SNMMI AI task-force guideline.
Figure 5
Figure 5
(A). Internal dosimetry workflow for dose calculation. (B). Three acquisition protocols to provide TAC for delineated targets on serial images. (C). Organ- and voxel-based dose calculation methods with their subsets.
See this image and copyright information in PMC

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