Issues and solutions in integrated radionuclide diagnosis and treatment

The integration of radionuclide diagnosis and treatment combines the dual functions of radionuclide imaging and treatment, and has been widely applied in the diagnosis and treatment of various tumors. Significant progress has been made in this field over the past few years, advancing tumor visualization for diagnostic assessment and precision treatment. However, issues such as inconsistent dose distribution between radionuclide imaging and therapy, short retention time of radionuclides, optimization of imaging radiation dose, and prediction of therapeutic dose remain prominent. This study introduces the current status and potential solutions to the above issues, including identifying different targets and probes, and screening patients sensitive to treatment, so as to improve the efficacy of radionuclide imaging and therapy. By modifying radionuclide imaging agents and using polymers or albumin conjugation, the retention time of radionuclides can be prolonged. Artificial intelligence is employed to reconstruct full-dose images or non-CT-attenuation-corrected images, thereby reducing imaging radiation dose. Machine learning models are utilized to optimize personalized therapeutic dose prediction. Overcoming these challenges can strongly promote the development of integrated radionuclide diagnosis and treatment.