A novel personalized time-varying biomechanical model for estimating lung tumor motion and deformation

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-09-03 DOI:10.1002/mp.18086

Liang Tan, Wenyou Hu, Liyuan Chen, Huanli Luo, Shi Li, Bin Feng, Xin Yang, Yongzhong Wu, Ying Wang, Fu Jin

{"title":"A novel personalized time-varying biomechanical model for estimating lung tumor motion and deformation","authors":"Liang Tan, Wenyou Hu, Liyuan Chen, Huanli Luo, Shi Li, Bin Feng, Xin Yang, Yongzhong Wu, Ying Wang, Fu Jin","doi":"10.1002/mp.18086","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Accurate prediction of lung tumor motion and deformation (LTMD) is essential for precise radiotherapy. However, existing models often rely on static, population-based material parameters, overlooking patient-specific and time-varying lung biomechanics. Personalized dynamic models that capture temporal changes in lung elasticity are needed to improve LTMD prediction and guide treatment planning more effectively.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>This study aims to develop a patient-specific, time-varying biomechanical model to predict LTMD more accurately.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Four-dimensional computed tomography (4DCT) images from 27 patients, each with 10 breathing phases, were analyzed. A finite element model was developed, modeling lung as a hyper-elastic material and tumor as linear elastic. Lung elasticity parameters, including Young's modulus (<i>E</i>) and Poisson's ratio (<i>v</i>), were optimized for each phase using Efficient Global Optimization algorithm. Four functions were tested to model the variation of <i>E</i> and <i>v</i> across different phases. For each patient, average values of these parameters were computed, and their correlation with 11 clinical features was analyzed. The model's accuracy in predicting LTMD was evaluated using tumor center of mass motion error (ΔTCM) and volumetric Dice similarity coefficient (vDSC). Factors influencing the model's accuracy were investigated. Specifically, lung surface traction vector fields (STVFs) were calculated during the transition from end-expiration to end-inspiration phases, and their relationship with LTMD was also analyzed.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The first-order Fourier function provided the best fit among four tested functions, with average R-squared values of 0.93 ± 0.03 for <i>E</i> and 0.91 ± 0.03 for <i>v</i>. The average values of <i>E</i> and <i>v</i> were significantly correlated with patient age. The model showed a mean ΔTCM of 1.47 ± 0.68 mm and a mean vDSC of 0.93 ± 0.02. A negative correlation was found between tumor deformation vDSC and ΔTCM (<i>r</i> = −0.55, <i>p</i> < 0.05). Higher STVFs were observed near diaphragm and intercostal muscles, with correlations between STVFs and tumor motion amplitude (<i>r</i> ≥ 0.92, <i>p</i> < 0.05).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>These findings offer new insights into developing personalized, time-varying motion management strategies of lung tumors.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 8","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18086","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical physics","FirstCategoryId":"3","ListUrlMain":"https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.18086","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Accurate prediction of lung tumor motion and deformation (LTMD) is essential for precise radiotherapy. However, existing models often rely on static, population-based material parameters, overlooking patient-specific and time-varying lung biomechanics. Personalized dynamic models that capture temporal changes in lung elasticity are needed to improve LTMD prediction and guide treatment planning more effectively.

Purpose

This study aims to develop a patient-specific, time-varying biomechanical model to predict LTMD more accurately.

Methods

Four-dimensional computed tomography (4DCT) images from 27 patients, each with 10 breathing phases, were analyzed. A finite element model was developed, modeling lung as a hyper-elastic material and tumor as linear elastic. Lung elasticity parameters, including Young's modulus (E) and Poisson's ratio (v), were optimized for each phase using Efficient Global Optimization algorithm. Four functions were tested to model the variation of E and v across different phases. For each patient, average values of these parameters were computed, and their correlation with 11 clinical features was analyzed. The model's accuracy in predicting LTMD was evaluated using tumor center of mass motion error (ΔTCM) and volumetric Dice similarity coefficient (vDSC). Factors influencing the model's accuracy were investigated. Specifically, lung surface traction vector fields (STVFs) were calculated during the transition from end-expiration to end-inspiration phases, and their relationship with LTMD was also analyzed.

Results

The first-order Fourier function provided the best fit among four tested functions, with average R-squared values of 0.93 ± 0.03 for E and 0.91 ± 0.03 for v. The average values of E and v were significantly correlated with patient age. The model showed a mean ΔTCM of 1.47 ± 0.68 mm and a mean vDSC of 0.93 ± 0.02. A negative correlation was found between tumor deformation vDSC and ΔTCM (r = −0.55, p < 0.05). Higher STVFs were observed near diaphragm and intercostal muscles, with correlations between STVFs and tumor motion amplitude (r ≥ 0.92, p < 0.05).

Conclusions

These findings offer new insights into developing personalized, time-varying motion management strategies of lung tumors.

Abstract Image

查看原文本刊更多论文

一种新的个性化时变生物力学模型用于估计肺肿瘤的运动和变形

背景准确预测肺肿瘤的运动和变形（LTMD）是精确放疗的必要条件。然而，现有的模型往往依赖于静态的、基于人群的材料参数，忽略了患者特异性和时变的肺生物力学。需要个性化的动态模型来捕捉肺弹性的时间变化，以提高LTMD的预测和更有效地指导治疗计划。本研究旨在建立一种针对患者的时变生物力学模型，以更准确地预测LTMD。方法对27例患者的4维计算机断层扫描（4DCT）图像进行分析。建立了一个有限元模型，将肺建模为超弹性材料，将肿瘤建模为线弹性材料。肺弹性参数，包括杨氏模量(E)和泊松比(v)，采用高效全局优化算法优化每个阶段。测试了四个函数来模拟E和v在不同阶段的变化。对每位患者计算这些参数的平均值，并分析其与11项临床特征的相关性。使用肿瘤质心运动误差（ΔTCM）和体积骰子相似系数（vDSC）评估模型预测LTMD的准确性。研究了影响模型精度的因素。具体而言，计算了呼气末到吸气末过渡阶段的肺表面牵引矢量场（STVFs），并分析了其与LTMD的关系。结果一阶傅里叶函数的拟合效果最好，E和v的平均r平方值分别为0.93±0.03和0.91±0.03，E和v的平均值与患者年龄有显著相关。模型平均ΔTCM为1.47±0.68 mm，平均vDSC为0.93±0.02。肿瘤变形vDSC与ΔTCM呈负相关（r = - 0.55, p < 0.05）。膈肌和肋间肌附近STVFs较高，STVFs与肿瘤运动幅值相关（r≥0.92,p < 0.05）。这些发现为制定个性化的、时变的肺肿瘤运动管理策略提供了新的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.