| Autor: |
Moraitis, Alexandros, Küper, Alina, Tran-Gia, Johannes, Eberlein, Uta, Chen, Yizhou, Seifert, Robert, Shi, Kuangyu, Kim, Moon, Herrmann, Ken, Fragoso Costa, Pedro, Kersting, David |
| Zdroj: |
Seminars in Nuclear Medicine; Jul2024, Vol. 54 Issue 4, p460-469, 10p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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