Chvetsov Alexei V, Yartsev Slav, Schwartz Jeffrey L, Mayr Nina
Department of Radiation Oncology, University of Washington, 1959 NE Pacific Street, Seattle, Washington 98195-6043.
London Regional Cancer Program, London Health Sciences Centre, 790 Commissioners Road East, London, Ontario 46A 4L6, Canada.
Med Phys. 2014 Jun;41(6):064101. doi: 10.1118/1.4875686.
In our previous work, the authors showed that a distribution of cell surviving fractions S2 in a heterogeneous group of patients could be derived from tumor-volume variation curves during radiotherapy for head and neck cancer. In this research study, the authors show that this algorithm can be applied to other tumors, specifically in nonsmall cell lung cancer. This new application includes larger patient volumes and includes comparison of data sets obtained at independent institutions.
Our analysis was based on two data sets of tumor-volume variation curves for heterogeneous groups of 17 patients treated for nonsmall cell lung cancer with conventional dose fractionation. The data sets were obtained previously at two independent institutions by using megavoltage computed tomography. Statistical distributions of cell surviving fractions S2 and clearance half-lives of lethally damaged cells T(1/2) have been reconstructed in each patient group by using a version of the two-level cell population model of tumor response and a simulated annealing algorithm. The reconstructed statistical distributions of the cell surviving fractions have been compared to the distributions measured using predictive assays in vitro.
Nonsmall cell lung cancer presents certain difficulties for modeling surviving fractions using tumor-volume variation curves because of relatively large fractional hypoxic volume, low gradient of tumor-volume response, and possible uncertainties due to breathing motion. Despite these difficulties, cell surviving fractions S2 for nonsmall cell lung cancer derived from tumor-volume variation measured at different institutions have similar probability density functions (PDFs) with mean values of 0.30 and 0.43 and standard deviations of 0.13 and 0.18, respectively. The PDFs for cell surviving fractions S2 reconstructed from tumor volume variation agree with the PDF measured in vitro.
The data obtained in this work, when taken together with the data obtained previously for head and neck cancer, suggests that the cell surviving fractions S2 can be reconstructed from the tumor volume variation curves measured during radiotherapy with conventional fractionation. The proposed method can be used for treatment evaluation and adaptation.
在我们之前的研究中,作者表明,头颈癌放疗期间肿瘤体积变化曲线可推导出异质性患者群体中的细胞存活分数S2分布。在本研究中,作者表明该算法可应用于其他肿瘤,特别是非小细胞肺癌。这一新应用纳入了更多患者,并对独立机构获得的数据集进行了比较。
我们的分析基于两个数据集,这些数据集来自17例接受常规剂量分割治疗的非小细胞肺癌异质性患者群体的肿瘤体积变化曲线。这些数据集先前由两个独立机构通过兆伏计算机断层扫描获得。通过使用肿瘤反应的两级细胞群体模型版本和模拟退火算法,在每个患者组中重建了细胞存活分数S2和致死性损伤细胞清除半衰期T(1/2)的统计分布。将重建的细胞存活分数统计分布与体外预测试验测量的分布进行了比较。
由于相对较大的部分缺氧体积、肿瘤体积反应的低梯度以及呼吸运动可能导致的不确定性,非小细胞肺癌在使用肿瘤体积变化曲线对存活分数进行建模时存在一定困难。尽管存在这些困难,但不同机构测量的肿瘤体积变化推导出的非小细胞肺癌细胞存活分数S2具有相似的概率密度函数(PDF),平均值分别为0.30和0.43,标准差分别为0.13和0.18。从肿瘤体积变化重建的细胞存活分数S2的PDF与体外测量的PDF一致。
这项工作获得的数据与先前头颈癌获得的数据一起表明,细胞存活分数S2可从常规分割放疗期间测量的肿瘤体积变化曲线重建。所提出的方法可用于治疗评估和调整。