Predicting Individual Tumor Response Dynamics in Locally Advanced Non-Small Cell Lung Cancer Radiation Therapy: A Mathematical Modelling Study

IF 6.4 1区 医学 Q1 ONCOLOGY
Sarah Barrett MSc , Mohammad U. Zahid PhD , Heiko Enderling PhD , Laure Marignol PhD
{"title":"Predicting Individual Tumor Response Dynamics in Locally Advanced Non-Small Cell Lung Cancer Radiation Therapy: A Mathematical Modelling Study","authors":"Sarah Barrett MSc ,&nbsp;Mohammad U. Zahid PhD ,&nbsp;Heiko Enderling PhD ,&nbsp;Laure Marignol PhD","doi":"10.1016/j.ijrobp.2024.10.038","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><div>To predict individual tumor responses to radiation therapy (RT) in non-small cell lung cancer.</div></div><div><h3>Materials and Methods</h3><div>The proliferation saturation index (PSI) model, which models tumor dynamics in response to RT as an instantaneous reduction in tumor volume, was fit to n = 162 patients with 4 distinct dose fractionation schedules (30-32 fractions × 2 Gy, 23-24 fractions × 2.75 Gy, 32-42 fractions × 1.8 Gy, and 30 fractions × 1.5 Gy Bidaily, followed by 5-12 fractions × 2 Gy daily). Following initial training, the predictive power of the model was tested using only the first 3 tumor volume measurements as measured on daily imaging. The remainder of tumor volume regression during RT was simulated using the PSI model. Comparisons of the measured to the simulated volumes were made using scatter plots, coefficient of determination (R<sup>2</sup>), and Pearson correlation coefficient values.</div></div><div><h3>Results</h3><div>The PSI model predicted tumor volume regression during RT with a high degree of accuracy. Comparison of the measured versus predicted volumes resulted in R<sup>2</sup> values of 0.968, 0.954, 0.968, and 0.937, and Pearson correlation coefficient values of 0.984, 0.977, 0.984, and 0.968 in the 2 Gy, 1.8 Gy, 2.75 Gy, and Bidaily groups, respectively.</div></div><div><h3>Conclusions</h3><div>The proliferation saturation model can predict, with a high degree of accuracy, non-small cell lung cancer tumor volume regression in response to RT in 4 distinct dose fractionation schedules.</div></div>","PeriodicalId":14215,"journal":{"name":"International Journal of Radiation Oncology Biology Physics","volume":"121 4","pages":"Pages 1077-1087"},"PeriodicalIF":6.4000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Radiation Oncology Biology Physics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360301624035636","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose

To predict individual tumor responses to radiation therapy (RT) in non-small cell lung cancer.

Materials and Methods

The proliferation saturation index (PSI) model, which models tumor dynamics in response to RT as an instantaneous reduction in tumor volume, was fit to n = 162 patients with 4 distinct dose fractionation schedules (30-32 fractions × 2 Gy, 23-24 fractions × 2.75 Gy, 32-42 fractions × 1.8 Gy, and 30 fractions × 1.5 Gy Bidaily, followed by 5-12 fractions × 2 Gy daily). Following initial training, the predictive power of the model was tested using only the first 3 tumor volume measurements as measured on daily imaging. The remainder of tumor volume regression during RT was simulated using the PSI model. Comparisons of the measured to the simulated volumes were made using scatter plots, coefficient of determination (R2), and Pearson correlation coefficient values.

Results

The PSI model predicted tumor volume regression during RT with a high degree of accuracy. Comparison of the measured versus predicted volumes resulted in R2 values of 0.968, 0.954, 0.968, and 0.937, and Pearson correlation coefficient values of 0.984, 0.977, 0.984, and 0.968 in the 2 Gy, 1.8 Gy, 2.75 Gy, and Bidaily groups, respectively.

Conclusions

The proliferation saturation model can predict, with a high degree of accuracy, non-small cell lung cancer tumor volume regression in response to RT in 4 distinct dose fractionation schedules.
预测局部晚期非小细胞肺癌放射治疗的个体肿瘤反应动力学:数学模型研究。
目的:预测非小细胞肺癌放射治疗(RT)的个体肿瘤反应。材料与方法:采用增殖饱和指数(PSI)模型,以肿瘤体积瞬时缩小为模型,对n = 162例患者进行了4种不同剂量分次方案的拟合(30-32分次× 2gy, 23-24分次× 2.75 Gy, 32-42分次× 1.8 Gy, 30分次× 1.5 Gy每日,随后5-12分次× 2gy每日)。在初始训练之后,仅使用每日成像测量的前3个肿瘤体积测量来测试模型的预测能力。RT期间剩余的肿瘤体积回归使用PSI模型进行模拟。采用散点图、决定系数(R2)和Pearson相关系数值对实测值与模拟体积进行比较。结果:PSI模型预测RT期间肿瘤体积的缩小具有较高的准确性。2 Gy、1.8 Gy、2.75 Gy和Bidaily组实测量与预测值的R2值分别为0.968、0.954、0.968和0.937,Pearson相关系数分别为0.984、0.977、0.984和0.968。结论:该增殖饱和度模型能较准确地预测4种不同剂量分级方案下非小细胞肺癌肿瘤体积对放疗的响应。
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
CiteScore
11.00
自引率
7.10%
发文量
2538
审稿时长
6.6 weeks
期刊介绍: International Journal of Radiation Oncology • Biology • Physics (IJROBP), known in the field as the Red Journal, publishes original laboratory and clinical investigations related to radiation oncology, radiation biology, medical physics, and both education and health policy as it relates to the field. This journal has a particular interest in original contributions of the following types: prospective clinical trials, outcomes research, and large database interrogation. In addition, it seeks reports of high-impact innovations in single or combined modality treatment, tumor sensitization, normal tissue protection (including both precision avoidance and pharmacologic means), brachytherapy, particle irradiation, and cancer imaging. Technical advances related to dosimetry and conformal radiation treatment planning are of interest, as are basic science studies investigating tumor physiology and the molecular biology underlying cancer and normal tissue radiation response.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信