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 visualiza...

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Main Author: HONG Yena, ZHANG Yü, SHI Kuangyu, LI Biao, GUO Rui
Format: Article
Language:zho
Published: Editorial Office of Journal of Diagnostics Concepts & Practice 2025-06-01
Series:Zhenduanxue lilun yu shijian
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Online Access:https://www.qk.sjtu.edu.cn/jdcp/fileup/1671-2870/PDF/1756094050220-1956721890.pdf
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author HONG Yena, ZHANG Yü, SHI Kuangyu, LI Biao, GUO Rui
author_facet HONG Yena, ZHANG Yü, SHI Kuangyu, LI Biao, GUO Rui
author_sort HONG Yena, ZHANG Yü, SHI Kuangyu, LI Biao, GUO Rui
collection DOAJ
description 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.
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institution Kabale University
issn 1671-2870
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publisher Editorial Office of Journal of Diagnostics Concepts & Practice
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series Zhenduanxue lilun yu shijian
spelling doaj-art-9322bb3210b44a9eb07ea52cfe3260a52025-08-26T01:50:28ZzhoEditorial Office of Journal of Diagnostics Concepts & PracticeZhenduanxue lilun yu shijian1671-28702025-06-01240326326710.16150/j.1671-2870.2025.03.004Issues and solutions in integrated radionuclide diagnosis and treatmentHONG Yena, ZHANG Yü, SHI Kuangyu, LI Biao, GUO Rui01. Department of Nuclear Medicine, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;2. Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern 3010, SwitzerlandThe 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.https://www.qk.sjtu.edu.cn/jdcp/fileup/1671-2870/PDF/1756094050220-1956721890.pdf|radionuclide|integrated diagnosis and treatment|dose optimization|dose prediction|artificial intelligence
spellingShingle HONG Yena, ZHANG Yü, SHI Kuangyu, LI Biao, GUO Rui
Issues and solutions in integrated radionuclide diagnosis and treatment
Zhenduanxue lilun yu shijian
|radionuclide|integrated diagnosis and treatment|dose optimization|dose prediction|artificial intelligence
title Issues and solutions in integrated radionuclide diagnosis and treatment
title_full Issues and solutions in integrated radionuclide diagnosis and treatment
title_fullStr Issues and solutions in integrated radionuclide diagnosis and treatment
title_full_unstemmed Issues and solutions in integrated radionuclide diagnosis and treatment
title_short Issues and solutions in integrated radionuclide diagnosis and treatment
title_sort issues and solutions in integrated radionuclide diagnosis and treatment
topic |radionuclide|integrated diagnosis and treatment|dose optimization|dose prediction|artificial intelligence
url https://www.qk.sjtu.edu.cn/jdcp/fileup/1671-2870/PDF/1756094050220-1956721890.pdf
work_keys_str_mv AT hongyenazhangyushikuangyulibiaoguorui issuesandsolutionsinintegratedradionuclidediagnosisandtreatment