Moraitis, Alexandros; Küper, Alina; Tran-Gia, Johannes; Eberlein, Uta; Chen, Yizhou; Seifert, Robert; Shi, Kuangyu; Kim, Moon; Herrmann, Ken; Fragoso Costa, Pedro; Kersting, David (2024). Future Perspectives of Artificial Intelligence in Bone Marrow Dosimetry and Individualized Radioligand Therapy. (In Press). Seminars in nuclear medicine Elsevier 10.1053/j.semnuclmed.2024.06.003
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Radioligand therapy is an emerging and effective treatment option for various types of malignancies, but may be intricately linked to hematological side effects such as anemia, lymphopenia or thrombocytopenia. The safety and efficacy of novel theranostic agents, targeting increasingly complex targets, can be well served by comprehensive dosimetry. However, optimization in patient management and patient selection based on risk-factors predicting adverse events and built upon reliable dose-response relations is still an open demand. In this context, artificial intelligence methods, especially machine learning and deep learning algorithms, may play a crucial role. This review provides an overview of upcoming opportunities for integrating artificial intelligence methods into the field of dosimetry in nuclear medicine by improving bone marrow and blood dosimetry accuracy, enabling early identification of potential hematological risk-factors, and allowing for adaptive treatment planning. It will further exemplify inspirational success stories from neighboring disciplines that may be translated to nuclear medicine practices, and will provide conceptual suggestions for future directions. In the future, we expect artificial intelligence-assisted (predictive) dosimetry combined with clinical parameters to pave the way towards truly personalized theranostics in radioligand therapy.
Item Type: |
Journal Article (Review Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine |
UniBE Contributor: |
Shi, Kuangyu |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1558-4623 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
17 Jul 2024 13:44 |
Last Modified: |
18 Jul 2024 10:21 |
Publisher DOI: |
10.1053/j.semnuclmed.2024.06.003 |
PubMed ID: |
39013673 |
BORIS DOI: |
10.48350/199053 |
URI: |
https://boris.unibe.ch/id/eprint/199053 |
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