放射性核素诊疗一体化的若干问题及对策

doi:10.16150/j.1671-2870.2025.03.004

诊断学理论与实践 ›› 2025, Vol. 24 ›› Issue (03): 263-267.doi: 10.16150/j.1671-2870.2025.03.004

放射性核素诊疗一体化的若干问题及对策

洪烨娜¹, 张宇¹, 李彪¹, 郭睿¹()

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

收稿日期:2024-11-04 接受日期:2025-02-08 出版日期:2025-06-25 发布日期:2025-06-25
通讯作者: 郭睿 E-mail：gr11734@rih.com.cn
基金资助:
国家自然科学基金面上项目(82171975)

Issues and solutions in integrated radionuclide diagnosis and treatment

HONG Yena¹, ZHANG Yü¹, SHI Kuangyu², LI Biao¹, GUO Rui¹()

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:2024-11-04 Accepted:2025-02-08 Published:2025-06-25 Online:2025-06-25

摘要/Abstract

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

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

中图分类号:

R446

洪烨娜, 张宇, 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.

HONG Yena, ZHANG Yü, SHI Kuangyu, LI Biao, GUO Rui. Issues and solutions in integrated radionuclide diagnosis and treatment[J]. Journal of Diagnostics Concepts & Practice, 2025, 24(03): 263-267.

图/表 1

参考文献 25

[1]	FUNKHOUSER J. Reinventing pharma: the theranostic revolution[J]. Curr Drug Discov, 2002(8):17-19.
[2]	WEBER W A, BARTHEL H, BENGEL F, et al. What Is Theranostics?[J]. J Nucl Med, 2023, 64(5):669-670.
[3]	宋祥铭, 吕小迎, 兰晓莉. 放射性核素诊疗一体化临床研究进展[J]. 中国医学影像技术, 2024, 40(01):116-120.
	SONG X M, LV X Y, LAN X L. Clinical research progress of radionuclide theranostics[J]. Chin J Med Imaging Technol, 2024, 40(1):116-120.
[4]	ZHANG S Q, WANG X K, GAO X, et al. Radiopharmaceuticals and their applications in medicine[J]. Signal Transduct Target Ther, 2025, 10(1):1.
[5]	KARIMZADEH A, HECK M, TAUBER R, et al. 177Lu-PSMA-I&T for treatment of metastatic castration-resistant prostate cancer: prognostic value of scintigraphic and clinical biomarkers[J]. J Nucl Med, 2023, 64(3):402-409.
[6]	RUZZEH S, ABDIKADIR A S, PAEZ D, et al. Therapeutic potential of FAPI RLT in oncology: a systematic review[J]. Theranostics, 2025, 15(9):4084-4100.
[7]	ZHAO L, KANG F, PANG Y Z, et al. Fibroblast activation protein inhibitor tracers and their preclinical, translational, and clinical status in China[J]. J Nucl Med, 2024, 65(Suppl 1):4S-11S.
[8]	ZHONG X, GUO J R, HAN X P, et al. Synthesis and preclinical evaluation of a novel FAPI-04 dimer for cancer theranostics[J]. Mol Pharm, 2023, 20(5):2402-2414.
[9]	PANG Y Z, ZHAO L, FANG J Y, et al. Development of FAPI tetramers to improve tumor uptake and efficacy of FAPI radioligand therapy[J]. J Nucl Med, 2023, 64(9):1449-1455.
[10]	ZBORALSKI D, HOEHNE A, BREDENBECK A, et al. Preclinical evaluation of FAP-2286 for fibroblast activation protein targeted radionuclide imaging and therapy[J]. J Nucl Med, 2022, 49(11):3651-3667.
[11]	PANG Y Z, ZHAO L, MENG T H, et al. PET imaging of fibroblast activation protein in various types of cancer using ⁶⁸Ga-FAP-2286: comparison with ¹⁸F-FDG and ⁶⁸Ga-FAPI-46 in a single-center, prospective study[J]. J Nucl Med, 2023, 64(3):386-394.
[12]	MILLUL J, KOEPKE L, HARIDAS G R, et al. Head-to-head comparison of different classes of FAP radioligands designed to increase tumor residence time: monomer, dimer, albumin binders, and small molecules vs peptides[J]. Eur J Nucl Med Mol Imaging, 2023, 50(10):3050-3061.
[13]	ZHANG Q Y, HU Y Y, ZHOU C, et al. Reducing pediatric total-body PET/CT imaging scan time with multimodal artificial intelligence technology[J]. EJNMMI Phys, 2024, 11(1):1. doi: 10.1186/s40658-023-00605-z pmid: 38165551
[14]	LIU G B, HU P C, YU H J, et al. Ultra-low-activity total-body dynamic PET imaging allows equal performance to full-activity PET imaging for investigating kinetic metrics of ¹⁸F-FDG in healthy volunteers[J]. Eur J Nucl Med Mol Imaging, 2021, 48(8):2373-2383.
[15]	TAN H, QI C, CAO Y Y, et al. Ultralow-dose [18F]FDG PET/CT imaging: demonstration of feasibility in dynamic and static images[J]. Eur Radiol, 2023, 33(7):5017-5027.
[16]	CHEN W Q, LIU L, LI Y H, et al. Evaluation of pediatric malignancies using total-body PET/CT with half-dose [18F]-FDG[J]. Eur J Nucl Med Mol Imaging, 2022, 49(12):4145-4155.
[17]	SARI H, TEIMOORISICHANI M, MINGELS C, et al. Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners[J]. Eur J Nucl Med Mol Imaging, 2022, 49(13):4490-4502.
[18]	MA R Y, HU J X, SARI H, et al. An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET[J]. Eur J Nucl Med Mol Ima-ging, 2022, 49(13):4464-4477.
[19]	ILAN E, SANDSTROM M, WASSBERG C, et al. Dose response of pancreatic neuroendocrine tumors treated with peptide receptor radionuclide therapy using ¹⁷⁷Lu-DOTATATE[J]. J Nucl Med, 2015, 56(2):177-182.
[20]	VIOLET J, JACKSON P, FERDINANDUS J, et al. Dosi-metry of ¹⁷⁷Lu-PSMA-617 in metastatic castration-resistant prostate cancer: correlations between pretherapeutic imaging and whole-body tumor dosimetry with treatment outcomes[J]. J Nucl Med, 2019, 60(4):517-523.
[21]	STEINHELFER L, LUNGER L, CALA L, et al. Long-term nephrotoxicity of ¹⁷⁷Lu-PSMA radioligand therapy[J]. J Nucl Med, 2024, 65(1):79-84.
[22]	PETERSON A B, WANG C, WONG K K, et al. ¹⁷⁷Lu-DOTATATE theranostics: predicting renal dosimetry from pretherapy ⁶⁸Ga-DOTATATE PET and clinical biomarkers[J]. Clin Nucl Med, 2023, 48(5):393-399.
[23]	AKHAVANALLAF A, PETERSON A B, FITZPATRICK K, et al. The predictive value of pretherapy [68Ga]Ga-DOTA-TATE PET and biomarkers in [177Lu]Lu-PRRT tumor dosimetry[J]. Eur J Nucl Med Mol Imaging, 2023, 50(10):2984-2996.
[24]	XUE S, GAFITA A, DONG C, et al. Application of machine learning to pretherapeutically estimate dosime-try in men with advanced prostate cancer treated with ¹⁷⁷Lu-PSMA I & T therapy[J]. Eur J Nucl Med Mol Ima-ging, 2022, 49(12): 4064-4072.
[25]	XUE S, GAFITA A, ZHAO Y, et al. Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics[J]. Eur J Nucl Med Mol Imaging, 2024,51:3450-3460.

放射性核素诊疗一体化的若干问题及对策

Issues and solutions in integrated radionuclide diagnosis and treatment

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 1

参考文献 25

相关文章 10

编辑推荐

Metrics

本文评价

[1]	徐梦迪, 高峰, 朱剑, 陈蕾, 秦雨萌, 黄越, 唐银萍, 沙杰. 新型海绵胶囊联合人工智能细胞DNA检测在早期食管癌筛查中的价值[J]. 诊断学理论与实践, 2024, 23(06): 580-586.
[2]	李卓含, 黄新韵, 郭睿, 李彪. ¹⁸F-FDG PET/CT在滤泡性淋巴瘤诊断和预后评估中的研究进展[J]. 诊断学理论与实践, 2024, 23(04): 439-444.
[3]	吴娜明, 李军, 陶娟. 恶性黑色素瘤的诊断热点[J]. 诊断学理论与实践, 2023, 22(03): 215-220.
[4]	唐静仪, 余群, 刘军. 结合人工智能的结构影像分析对阿尔茨海默病的早期预测及精准诊断研究进展[J]. 诊断学理论与实践, 2022, 21(01): 12-17.
[5]	徐浩, 张治, 解学乾, 杨文艺, 刘少稳. 冠脉生理功能评估软件（DEEPVESSEL FFR）与有创FFR在评估冠脉缺血中的对比研究[J]. 诊断学理论与实践, 2021, 20(04): 384-390.
[6]	许晶晶, 张敏鸣. 人工智能机器学习方法在阿尔茨海默病中的应用现状[J]. 诊断学理论与实践, 2018, 17(04): 466-470.
[7]	冯国伟, 周熠磊,. 多发性内分泌腺瘤病1型~(99m)Tc-MIBI垂体显像一例[J]. 诊断学理论与实践, 2015, 14(06): 565-566.
[8]	陈刚, 胡佳佳, 江旭峰, 张淼, 张一帆, 李彪,. ~(18)F-FDG PET/CT寻找多发性骨转移瘤原发灶的临床价值评估[J]. 诊断学理论与实践, 2014, 13(03): 308-311.
[9]	尹红燕, 周翔, 张一帆,. 放射性核素标记GLP-1及其类似物显像的研究进展[J]. 诊断学理论与实践, 2010, 9(01): 87-89.
[10]	吴靖川. 放射性核素在心肌缺血性病变中临床应用[J]. 诊断学理论与实践, 2005, 4(02): 91-92.