放射性核素诊疗一体化的若干问题及对策

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, SHI Kuangyu², 李彪¹, 郭睿¹()

1.开云网页登录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:2024-11-04Accepted:2025-02-08Published:2025-06-25Online: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, 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.

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 using⁶⁸Ga-FAP-2286: comparison with¹⁸F-FDG and⁶⁸Ga-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-zpmid: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 of¹⁸F-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 using¹⁷⁷Lu-DOTATATE[J].J Nucl Med,2015,56(2):177-182.
[20]	VIOLET J, JACKSON P, FERDINANDUS J, et al. Dosi-metry of¹⁷⁷Lu-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 of¹⁷⁷Lu-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 pretherapy⁶⁸Ga-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 with¹⁷⁷Lu-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.