功能影像引导质子FLASH治疗TCP及NTCP评估新方法探索

沈树成 ,  李梦瑶 ,  孙崧 ,  王若峥 ,  戴天缘 ,  尹勇

中国医学物理学杂志 ›› 2026, Vol. 43 ›› Issue (6) : 701 -709.

PDF (4327KB)
中国医学物理学杂志 ›› 2026, Vol. 43 ›› Issue (6) : 701 -709. DOI: 10.3969/j.issn.1005-202X.2026.06.001
医学放射物理

功能影像引导质子FLASH治疗TCP及NTCP评估新方法探索

作者信息 +

Functional imaging-based assessment of TCP and NTCP in proton FLASH radiotherapy

Author information +
文章历史 +
PDF (4430K)

摘要

目的:建立一种基于FMISO(Fluoromisonidazole)-PET功能影像的质子FLASH放疗肿瘤控制概率(TCP)和正常组织并发症概率(NTCP)评估方法,为质子FLASH放疗的疗效预测与临床决策提供依据。方法:基于氧耗竭动力学模型,建立FLASH效应评估模型,用于计算不同氧分压和剂量率条件下的体素级FLASH保护效应因子(FSE)。利用SUV-pO2(氧分压)映射模型将FMISO-PET摄取值转换为体素级氧分压分布。通过FSE将FLASH照射剂量(DFLASH)推算为常规剂量率下的等效剂量(Deq),并进行2 Gy等效剂量换算。采用广义等效均匀剂量整合靶区与危及器官的生物剂量分布,进行TCP和NTCP的量化评估。以非小细胞肺癌(NSCLC)患者为例,应用本研究建立的方法,预测TCP与放射性肺炎(CTCAE≥2级)风险。结果:(1)FSE呈显著氧依赖性:正常富氧组织(pO2≈40 mmHg)FSE≈1.9,而乏氧的肿瘤组织FSE≈1.0,两者差异显著。(2)在>40 Gy/s FLASH放疗条件下,NTCP较常规剂量率可相对降低约83%,而TCP降低不明显,可扩大肿瘤放射治疗窗。(3)两例NSCLC患者验证结果显示,FLASH条件下肿瘤TCP相对下降1%~21%。正常组织保护方面,外周型患侧肺NTCP相对降低约83%,中央型NSCLC患侧肺NTCP相对降低约88%、心脏NTCP相对降低约87%;中央型NSCLC因正常组织受照体积更大,FLASH保护获益更为显著。结论:本研究设计的方法实现FLASH效应量化及与临床预测模型的整合,为质子FLASH放疗的生物学效应评估与个体化治疗方案优化提供可靠依据。

Abstract

Objective To develop an FMISO (Fluoromisonidazole)-PET functional imaging-guided framework for assessing tumor control probability (TCP) and normal tissue complication probability (NTCP) in proton FLASH radiotherapy, thereby providing a quantitative basis for efficacy prediction and clinical decision-making. Methods A FLASH effect evaluation model was established based on oxygen depletion kinetics to calculate the voxel-level FLASH sparing effect (FSE) factor under varying oxygen partial pressures (pO2) and dose rates. An SUV-pO2 mapping model was utilized to convert FMISO-PET uptake values into voxel-wise pO2 distributions. The FLASH physical dose (D FLASH) was converted to the equivalent dose at conventional dose rate (D eq) using the FSE, and further converted to the equivalent dose in 2 Gy. The generalized equivalent uniform dose was employed to integrate the biological dose distributions of target volumes and organs-at-risk for quantitative assessment of TCP and NTCP. The proposed framework was applied to non-small cell lung cancer (NSCLC) patients to predict the TCP and the risk of radiation pneumonitis (CTCAE grade≥2). Results (1) The FSE exhibited distinct oxygen dependence, reaching approximately 1.9 in well-oxygenated normal tissues (pO2≈40 mmHg) but close to 1.0 in hypoxic tumor tissues, demonstrating a significant differential response. (2) Under FLASH radiotherapy conditions (>40 Gy/s), NTCP was reduced by approximately 83% relative to conventional dose rates, while the reduction in TCP was not significant, resulting in wider therapeutic window. (3) Validation in two NSCLC patients showed that under FLASH conditions, the tumor TCP decreased by 1% to 21%. Regarding normal tissue sparing, the peripheral NSCLC case exhibited a relative reduction of approximately 83% in ipsilateral lung NTCP. For the central NSCLC case, the relative reductions were approximately 88% for the ipsilateral lung NTCP and 87% for the cardiac NTCP. The FLASH sparing benefit was more pronounced in central NSCLC owing to the larger irradiated volume of normal tissues. Conclusion The proposed method enables quantification of the FLASH effect and its integration with clinical prediction models, providing a feasible framework for the biological effect assessment and personalized optimization for proton FLASH radiotherapy.

关键词

FLASH放疗 / 质子放疗 / 肿瘤控制概率 / 正常组织并发症概率 / 氧耗竭

Key words

FLASH radiotherapy / proton radiotherapy / tumor control probability / normal tissue complication probability / oxygen depletion

引用本文

引用格式 ▾
沈树成,李梦瑶,孙崧,王若峥,戴天缘,尹勇. 功能影像引导质子FLASH治疗TCP及NTCP评估新方法探索[J]. 中国医学物理学杂志, 2026, 43(6): 701-709 DOI:10.3969/j.issn.1005-202X.2026.06.001

登录浏览全文

4963

注册一个新账户 忘记密码

参考文献

[1]

Scarmelotto A, Delprat V, Michiels C, et al. The oxygen puzzle in FLASH radiotherapy: a comprehensive review and experimental outlook[J]. Clin Transl Radiat Oncol, 2024, 49: 100860.

[2]

Vozenin MC, Bourhis J, Durante M . Towards clinical translation of FLASH radiotherapy[J]. Nat Rev Clin Oncol, 2022, 19(12): 791-803.

[3]

Böhlen TT, Germond JF, Bourhis J, et al. Normal tissue sparing by FLASH as a function of single—fraction dose: a quantitative analysis[J]. Int J Radiat Oncol Biol Phys, 2022, 114(5): 1032-1044.

[4]

Luo H, Yang CL, Yue JB, et al. Consensus statement on the exploration of clinical translation and application of electron ultra—high dose rate FLASH radiotherapy[J]. Precis Radiat Oncol, 2025, 9(1): 4-12.

[5]

Ma YQ, Zhang WK, Zhao ZM, et al. Current views on mechanisms of the FLASH effect in cancer radiotherapy[J]. Natl Sci Rev, 2024, 11(10): nwae350.

[6]

Zheng DD, Preuss K, Milano MT, et al. Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review[J]. Radiat Oncol, 2025, 20(1): 49.

[7]

Yang XX, Luo H, Zhang JJ, Ge H, Ge L . Clinical translation of ultra—high dose rate flash radiotherapy: opportunities, challenges, and prospects[J]. World J Radiol, 2025, 17(4): 105722.

[8]

Alper T, Howard—Flanders P . Role of oxygen in modifying the radiosensitivity of E. coli B[J]. Nature, 1956, 178(4540): 978-979.

[9]

Fowler JF . The linear—quadratic formula and progress in fractionated radiotherapy[J]. Br J Radiol, 1989, 62(740): 679-694.

[10]

Toma—Dasu I, Uhrdin J, Antonovic L, et al. Dose prescription and treatment planning based on FMISO—PET hypoxia[J]. Acta Oncol, 2012, 51(2): 222-230.

[11]

Lazzeroni M, Ureba A, Rosenberg V, et al. Evaluating the impact of a rigid and a deformable registration method of pre—treatment images for hypoxia—based dose painting[J]. Phys Med, 2024, 122: 103376.

[12]

Van Dyk J, Mah K, Keane TJ . Radiation—induced lung damage: dose—time—fractionation considerations[J]. Radiother Oncol, 1989, 14(1): 55-69.

[13]

Niemierko A. Reporting and analyzing dose distributions: a concept of equivalent uniform dose[J]. Med Phys, 1997, 24(1): 103-110.

[14]

Niemierko A. A generalized concept of equivalent uniform dose(EUD)[J]. Med Phys, 1999, 26(6): 1100.

[15]

Kutcher GJ, Burman C . Calculation of complication probability factors for non—uniform normal tissue irradiation: the effective volume method[J]. Int J Radiat Oncol Biol Phys, 1989, 16(6): 1623-1630.

[16]

Seppenwoolde Y, Lebesque JV, de Jaeger K, et al. Comparing different NTCP models that predict the incidence of radiation pneumonitis[J]. Int J Radiat Oncol Biol Phys, 2003, 55(3): 724-735.

[17]

Petersson K, Adrian G, Butterworth K, et al. A quantitative analysis of the role of oxygen tension in FLASH radiation therapy[J]. Int J Radiat Oncol Biol Phys, 2020, 107(3): 539-547.

[18]

Li XQ, Ding XF, Zheng WL, et al. Linear energy transfer incorporated spot—scanning proton arc therapy optimization: a feasibility study[J]. Front Oncol, 2021, 11: 698537.

[19]

Pennock M, Wei SY, Cheng C, et al. Proton Bragg peak FLASH enables organ sparing and ultra—high dose—rate delivery: proof of principle in recurrent head and neck cancer[J]. Cancers (Basel), 2023, 15(15): 3828.

[20]

Wenzl T, Wilkens JJ . Modelling of the oxygen enhancement ratio for ion beam radiation therapy[J]. Phys Med Biol, 2011, 56(11): 3251-3268.

[21]

Toma—Dasu I, Uhrdin J, Daşu A, et al. Therapy optimization based on non—linear uptake of PET tracers versus "linear dose painting" [C]// World Congress on Medical Physics and Biomedical Engineering, September 7—12, 2009, Munich, Germany. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009: 221-224.

[22]

Okunieff P, Morgan D, Niemierko A, et al. Radiation dose—response of human tumors[J]. Int J Radiat Oncol Biol Phys, 1995, 32(4): 1227-1237.

[23]

Mahmoudi F, Chegeni N, Bagheri A, et al. Optimization of the dose—volume effect parameter "a" in EUD—based TCP models for breast cancer radiotherapy[J]. Technol Cancer Res Treat. (2025—03—31). https://doi.org/10.1177/15330338251329103.

[24]

Song H, Kim Y, Sung W . Modeling of the FLASH effect for ion beam radiation therapy[J]. Phys Med, 2023, 108: 102553.

基金资助

国家自然科学基金(12575365)

国家自然科学基金(12275162)

国家自然科学基金(12105160)

山东省泰山学者工程(ts201712098)

山东省泰山学者工程(tsqn202507373)

新疆维吾尔自治区重点研发计划(2022B03019-5)

山东省高等学校青年创新团队发展计划(2024KJJ013)

山东省青年科技人才托举工程(SDAST2024QTB034)

山东省自然科学基金(ZR2021QA099)

AI Summary AI Mindmap
PDF (4327KB)

4

访问

0

被引

详细

导航
相关文章

AI思维导图

/