诊断学理论与实践

诊断学理论与实践 >

2025 , Vol. 24 >Issue 03: 263 - 267

DOI: https://doi.org/10.16150/j.1671-2870.2025.03.004

国内外学术动态

放射性核素诊疗一体化的若干问题及对策

  • 洪烨娜 ,
  • 张宇 ,
  • SHI Kuangyu ,
  • 李彪 ,
  • 郭睿
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  • 1.上海交通大学医学院附属瑞金医院核医学科，上海 200025
    2.瑞士伯尔尼大学医院核医学科，瑞士 伯尔尼 3010
郭睿 E-mail：gr11734@rih.com.cn

收稿日期: 2024-11-04

  录用日期: 2025-02-08

  网络出版日期: 2025-06-25

基金资助

国家自然科学基金面上项目(82171975)

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Issues and solutions in integrated radionuclide diagnosis and treatment

  • HONG Yena ,
  • ZHANG Yü ,
  • SHI Kuangyu ,
  • LI Biao ,
  • GUO Rui
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  • 1. 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, Switzerland

Received date: 2024-11-04

  Accepted date: 2025-02-08

  Online published: 2025-06-25

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摘要

放射性核素诊疗一体化结合了核素显像和治疗的双重功能，已被广泛应用于多种肿瘤的诊断及治疗。这一领域在过去几年取得了显著进展，推动了肿瘤可视化诊断评估和精准治疗。然而，核素显像和治疗之间剂量分布不一致、核素滞留时间短、显像辐射剂量的优化、治疗剂量的预测等问题仍较为突出。本文介绍上述问题的现状及潜在解决方案，包括寻找不同的靶点、不同的探针、筛选对治疗敏感的患者，以提高核素显像和治疗效果；通过改良放射性核素显像剂，采用多聚体或白蛋白连接延长核素滞留时间；采用人工智能技术还原全剂量显像图像或无CT衰减校正图像来减少显像辐射剂量；采用机器学习模型优化个体化治疗剂量预测。这些挑战的克服能够有力推动核素诊疗一体化的发展。

关键词： 放射性核素; 诊疗一体化; 剂量优化; 剂量预测; 人工智能

本文引用格式

洪烨娜 , 张宇 , SHI Kuangyu , 李彪 , 郭睿 . 放射性核素诊疗一体化的若干问题及对策[J]. 诊断学理论与实践, 2025 , 24(03) : 263 -267 . DOI: 10.16150/j.1671-2870.2025.03.004

Abstract

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.

Key words： Radionuclide; Integrated diagnosis and treatment; Dose optimization; Dose prediction; Artificial intelligence

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