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Zamani-Siahkali N, Mirshahvalad SA, Farbod A, Divband G, Pirich C, Veit-Haibach P, Cook G, Beheshti M. SPECT/CT, PET/CT, and PET/MRI for Response Assessment of Bone Metastases. Semin Nucl Med 2024; 54:356-370. [PMID: 38172001 DOI: 10.1053/j.semnuclmed.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024]
Abstract
Recent developments in hybrid SPECT/CT systems and the use of cadmium-zinc-telluride (CZT) detectors have improved the diagnostic accuracy of bone scintigraphy. These advancements have paved the way for novel quantitative approaches to accurate and reproducible treatment monitoring of bone metastases. PET/CT imaging using [18F]F-FDG and [18F]F-NaF have shown promising clinical utility in bone metastases assessment and monitoring response to therapy and prediction of treatment response in a broad range of malignancies. Additionally, specific tumor-targeting tracers like [99mTc]Tc-PSMA, [68Ga]Ga-PSMA, or [11C]C- or [18F]F-Choline revealed high diagnostic performance for early assessment and prognostication of bone metastases, particularly in prostate cancer. PET/MRI appears highly accurate imaging modality, but has associated limitations notably, limited availability, more complex logistics and high installation costs. Advances in artificial intelligence (Al) seem to improve the accuracy of imaging modalities and provide an assistant role in the evaluation of treatment response of bone metastases.
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Affiliation(s)
- Nazanin Zamani-Siahkali
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mirshahvalad
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Canada
| | - Abolfazl Farbod
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Canada
| | - Gary Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Cook GJR, Thorpe MP. Bone Metastases. Cancer J 2024; 30:202-209. [PMID: 38753755 DOI: 10.1097/ppo.0000000000000717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
ABSTRACT Bone metastases occur frequently in common malignancies such as breast and prostate cancer. They are responsible for considerable morbidity and skeletal-related events. Fortunately, there are now several systemic, focal, and targeted therapies that can improve quality and length of life, including radionuclide therapies. It is therefore important that bone metastases can be detected as early as possible and that treatment can be accurately and sensitively monitored. Several bone-specific and tumor-specific single-photon emission computed tomography and positron emission tomography molecular imaging agents are available, for detection and monitoring response to systemic therapeutics, as well as theranostic agents to confirm target expression and predict response to radionuclide therapies.
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Affiliation(s)
- Gary J R Cook
- From the Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Matthew P Thorpe
- Division of Nuclear Radiology, Department of Radiology, Mayo Clinic, Rochester, MN
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Belge Bilgin G, Bilgin C, Burkett BJ, Orme JJ, Childs DS, Thorpe MP, Halfdanarson TR, Johnson GB, Kendi AT, Sartor O. Theranostics and artificial intelligence: new frontiers in personalized medicine. Theranostics 2024; 14:2367-2378. [PMID: 38646652 PMCID: PMC11024845 DOI: 10.7150/thno.94788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/17/2024] [Indexed: 04/23/2024] Open
Abstract
The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.
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Affiliation(s)
| | - Cem Bilgin
- Department of Radiology, Mayo Clinic Rochester, MN, USA
| | | | - Jacob J. Orme
- Department of Oncology, Mayo Clinic Rochester, MN, USA
| | | | | | | | - Geoffrey B Johnson
- Department of Radiology, Mayo Clinic Rochester, MN, USA
- Department of Immunology, Mayo Clinic Rochester, MN, USA
| | | | - Oliver Sartor
- Department of Radiology, Mayo Clinic Rochester, MN, USA
- Department of Oncology, Mayo Clinic Rochester, MN, USA
- Department of Urology, Mayo Clinic Rochester, MN, USA
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