预后导向的肺癌调强放疗计划优化新方法

doi:10.12122/j.issn.1673-4254.2025.03.22

南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (3): 643-649.doi: 10.12122/j.issn.1673-4254.2025.03.22

• • 上一篇

预后导向的肺癌调强放疗计划优化新方法

李华莉¹(), 宋婷¹, 刘嘉雯¹, 李永宝², 姜兆静⁴, 窦文⁴, 周凌宏¹^,³()

^1.南方医科大学生物医学工程学院，广东广州 510515
^2.中山大学肿瘤防治中心，华南肿瘤学国家重点实验室，广东广州 510060
^3.南方医科大学附属广东省人民医院（广东省医学科学院）放疗科，广东广州 510080
^4.南方医科大学珠江医院，广东广州 510280

收稿日期:2024-11-26 出版日期:2025-03-20 发布日期:2025-03-28
通讯作者: 周凌宏 E-mail:3178010219@i.smu.edu.cn;smart@smu.edu.cn
作者简介:李华莉，在读硕士研究生，E-mail: 3178010219@i.smu.edu.cn
基金资助:
国家自然科学基金(82472117);广东省基础与应用基础研究基金(2024A1515011831)

Prognosis-guided optimization of intensity-modulated radiation therapy plans for lung cancer

Huali LI¹(), Ting SONG¹, Jiawen LIU¹, Yongbao LI², Zhaojing JIANG⁴, Wen DOU⁴, Linghong ZHOU¹^,³()

^1.Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
^2.State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
^3.Department of Radiotherapy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
^4.Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China

Received:2024-11-26 Online:2025-03-20 Published:2025-03-28
Contact: Linghong ZHOU E-mail:3178010219@i.smu.edu.cn;smart@smu.edu.cn
Supported by:
National Natural Science Foundation of China(82472117)

摘要/Abstract

摘要：

目的以肺癌行调强放疗为研究对象，提出一种将预后预测模型纳入放疗计划逆向优化框架中，直接引导放疗计划向预后风险最小方向逼近的调强放疗计划新优化方法。方法构建以最小预后风险为主目标函数、临床剂量限值为约束条件的混合通量分布优化模型，预后风险事件包括肿瘤局部控制失败、放射性心脏毒性发生和放射性肺炎二级以上事件发生。以3种预后预测概率加权形式构建总风险概率模型，并以最小化总风险概率为主目标项、靶区剂量均匀性为次目标项、常见指南约定的计划感兴趣区域特定剂量统计指征项为约束条件。为验证优化方法的有效性，实验选取15例非小细胞肺癌调强放射治疗计划，分别采用所提出计划逆向优化方法（本研究计划）和现行的基于剂量-体积限值的计划优化方法（临床参考计划）生成计划，比较不同计划在临床关注的剂量学指征项与预后预测项上的计划质量差异。结果本研究计划较临床参考计划整体可达相近的物理剂量统计水平，同时理论模型计算的预期预后结果有所改善。本研究计划靶区的D_95%基本与临床计划保持一致（100.33% vs 102.57%，P=0.056），心脏和肺的平均剂量从9.83、9.50 Gy降低至7.02、8.40 Gy，差异具有统计学意义（t=4.537、4.104，P<0.05）。与临床计划相比，所提方法优化得到计划通过理论模型预测的局部控制失败概率相近（60.05% vs 59.66%），同时放射性心脏毒性概率降低1.41%。结论本研究所提出的纳入预后预测模型引导的肺癌调强放疗计划自动优化新方法，可以兼容现有放疗计划设计需满足循证医学体系下的物理剂量统计分布要求，同时进一步优化患者的预期预后，为实现肿瘤患者更高预后、更优生存质量的个性化放疗奠定技术基础。

关键词: 预后引导计划优化, 调强放疗计划, 计划逆向优化

Abstract:

Objective To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. Methods A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. Results In terms of the dosemetric indicators, D_95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. Conclusion The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.

Key words: prognosis-guided treatment planning optimization, intensity-modulated radiation therapy, inverse planning optimization

李华莉, 宋婷, 刘嘉雯, 李永宝, 姜兆静, 窦文, 周凌宏. 预后导向的肺癌调强放疗计划优化新方法[J]. 南方医科大学学报, 2025, 45(3): 643-649.

Huali LI, Ting SONG, Jiawen LIU, Yongbao LI, Zhaojing JIANG, Wen DOU, Linghong ZHOU. Prognosis-guided optimization of intensity-modulated radiation therapy plans for lung cancer[J]. Journal of Southern Medical University, 2025, 45(3): 643-649.

图/表 7

表1 用于构建预后目标的预后预测模型

Tab.1 The prognostic prediction model used to develop outcome-based objective

Model	Form	Dose metric	Clinical metric
LRF^［17］	Cox	Average BED for PTV	Age, Sex, Stage, Combined chemotherapy or not
RCT^［18］	Cox	Average EQD2 for heart	Previous cardiac history
RP≥2^［19］	Logistic	Average EQD2 for lungs	Age, Chemotherapy, Smoke

图1 1例肺癌患者不同计划的DVH与横截面剂量分布比较

Fig.1 Comparison of dose-volume histogram (DVH) and transversal dose distribution in a case of lung cancer. A: DVH. B: Reference clinical plan. C: Proposed plan.

图2 15例患者本研究计划与临床参考计划的剂量量化指标比较

Fig.2 Comparison of dosimetric parameters in different plans for 15 lung cancer cases. A: Average DVH with 95% confidence interval. B: Dosimetric indices. *: Absolute dose (Gy). Dx%: Relative dose received by x% of the volume; Vx Gy: Relative volume receiving at least x Gy; Dmax: Maximum dose; Dmean: Mean dose.

表2 15例肺癌患者不同计划的平均剂量学指征比较

Tab.2 Comparison of average dosimetric indications for 15 lung cancer cases (Mean±SD)

ROI	Metric	Constraint	Clinical plan	Proposed plan	t/z	P
PTV	D_95%(%)	≥98	100.33±1.93	102.57±3.30	-2.087	0.056
PTV	D_98%(%)	-	95.52±5.23	99.80±2.66	-1.647	0.100^a
Lungs	V_20Gy(%)	≤30	18.21±6.23	16.74±7.12	2.007	0.064
	V_10Gy(%)	≤45	29.09±11.03	26.27±8.91	3.216	0.006
	V_5Gy(%)	≤60	40.40±14.75	34.98±13.15	2.759	0.015
	D_mean(Gy)	-	9.50±3.24	8.40±4.01	4.104	0.001
Heart	V_30Gy(%)	≤40	13.4±16.07	7.83±11.90	-2.845	0.004^a
	V_40Gy(%)	≤30	7.21±10.46	4.18±8.51	-2.803	0.005^a
	D_mean(Gy)	-	9.83±9.41	7.02±8.16	4.537	<0.001
Esophagus	D_max(Gy)	≤105	74.51±39.51	80.19±38.48	-0.722	0.470^a
Esophagus	V_60%(%)	-	1.20±3.27	0.93±2.24	-0.338	0.735^a
Spinal cord	D_max(Gy)	≤45	31.49±15.49	30.11±14.04	0.809	0.432

表3 15例肺癌患者不同计划的预后事件预测发生率比较

Tab.3 Comparison of average predicted rates of prognostic events in 15 lung cancer cases (Mean±SD, %)

Prognostic	Clinical plan	Proposed plan	t/z	P
LRF	60.05±12.38	59.66±11.88	1.053	0.310
CE	7.99±5.20	6.55±3.97	-3.296	0.001^a
RP	3.84±1.60	3.59±2.08	-2.556	0.011^a
TCP	57.37±11.26	57.95±11.36	-0.614	0.549
NTCP_heart	3.67±5.78	2.20±5.56	-3.045	0.002^a
NTCP_lung	2.56±2.10	2.34±2.92	-2.556	0.011^a

图3 15例肺癌患者肿瘤预后项与危及器官预后项预测差异对比

Fig.3 Comparison of predicted prognosis probability among different plans for 15 lung cancer patients. A: ∆PLRF vs ∆PRP/∆PRCT . B. ∆PLRF vs ∆(PRP+PRCT). ∆= item of clinical plan-item of proposed plan; The direction of the black arrow indicates that the proposed plan have a better prognosis. LRF: Local control failure; RCT: Radiation-induced cardiac toxicity; RP: Radiation pneumonitis.

图4 1例肺癌患者不同计划的剂量量化指标比较

Fig.4 Comparison of dosimetric parameters in different plans for a lung cancer case. A: DVH. B: Dosimetric indices.

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预后导向的肺癌调强放疗计划优化新方法

Prognosis-guided optimization of intensity-modulated radiation therapy plans for lung cancer

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