Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Semin Nucl Med. 2024 Jul;54(4):460-469. doi: 10.1053/j.semnuclmed.2024.06.003. Epub 2024 Jul 15.
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.
放射性配体治疗是一种新兴且有效的治疗各种恶性肿瘤的方法，但可能与贫血、淋巴细胞减少症或血小板减少症等血液学副作用密切相关。针对日益复杂的靶点的新型治疗药物的安全性和疗效可以通过全面的剂量学来很好地评估。然而，基于预测不良反应的风险因素并建立在可靠的剂量-反应关系的基础上，优化患者管理和患者选择仍然是一个未满足的需求。在这种情况下，人工智能方法，特别是机器学习和深度学习算法，可能会发挥关键作用。本文综述了通过提高骨髓和血液剂量学准确性、早期识别潜在血液学风险因素以及实现自适应治疗计划，将人工智能方法整合到核医学剂量学领域的新机遇。此外，还将举例说明来自相邻学科的鼓舞人心的成功案例，这些案例可能会被转化为核医学实践，并为未来的方向提供概念性建议。未来，我们预计人工智能辅助（预测）剂量学与临床参数相结合，将为放射性配体治疗中的真正个体化治疗铺平道路。
