Samadi Miandoab Payam, Worm Esben, Hansen Rune, Weber Britta, Høyer Morten, Saramad Shahyar, Setayeshi Saeed, Poulsen Per Rugaard
Department of Oncology, Aarhus University Hospital, Aarhus, Denmark.
Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
Front Oncol. 2024 Sep 24;14:1470650. doi: 10.3389/fonc.2024.1470650. eCollection 2024.
This study investigates different strategies for estimating internal liver tumor motion during radiotherapy based on continuous monitoring of external respiratory motion combined with sparse internal imaging.
Fifteen patients underwent three-fraction stereotactic liver radiotherapy. The 3D internal tumor motion (INT) was monitored by electromagnetic transponders while a camera monitored the external marker block motion (EXT). The ability of four external-internal correlation models (ECM) to estimate INT as function of EXT was investigated: a simple linear model (ECM1), an augmented linear model (ECM2), an augmented quadratic model (ECM3), and an extended quadratic model (ECM4). Each ECM was constructed by fitting INT and EXT during the first 60s of each fraction. The fit accuracy was calculated as the root-mean-square error (RMSE) between ECM-estimated and actual tumor motion. Next, the RMSE of the ECM-estimated tumor motion throughout the fractions was calculated for four simulated ECM update strategies: (A) no update, 0.33Hz internal sampling with continuous update of either (B) all ECM parameters based on the last 2 minutes samples or (C) only the baseline term based on the last 5 samples, (D) full ECM update every minute using 20s continuous internal sampling.
The augmented quadratic ECM3 had best fit accuracy with mean (± SD)) RMSEs of 0.32 ± 0.11mm (left-right, LR), 0.79 ± 0.30mm (cranio-caudal, CC) and 0.56 ± 0.31mm (anterior-posterior, AP). However, the simpler augmented linear ECM2 combined with frequent baseline updates (update strategy C) gave best motion estimations with mean RMSEs of 0.41 ± 0.14mm (LR), 1.02 ± 0.33mm (CC) and 0.78 ± 0.48mm (AP). This was significantly better than all other ECM-update strategy combinations for CC motion (Wilcoxon signed rank p<0.05).
The augmented linear ECM2 combined with frequent baseline updates provided the best compromise between fit accuracy and robustness towards irregular motion. It allows accurate internal motion monitoring by combining external motioning with sparse 0.33Hz kV imaging, which is available at conventional linacs.
本研究基于对外部呼吸运动的持续监测结合稀疏的内部成像，探究了放疗期间估计肝脏内部肿瘤运动的不同策略。
15名患者接受了三分割立体定向肝脏放疗。通过电磁应答器监测三维内部肿瘤运动（INT），同时用摄像头监测外部标记块运动（EXT）。研究了四种外部-内部相关模型（ECM）根据EXT估计INT的能力：简单线性模型（ECM1）、增强线性模型（ECM2）、增强二次模型（ECM3）和扩展二次模型（ECM4）。每个ECM通过在每个分割的前60秒拟合INT和EXT来构建。拟合精度计算为ECM估计的肿瘤运动与实际肿瘤运动之间的均方根误差（RMSE）。接下来，针对四种模拟的ECM更新策略，计算各分割中ECM估计的肿瘤运动的RMSE：（A）不更新，（B）基于最后2分钟样本对所有ECM参数进行连续更新，（C）仅基于最后5个样本对基线项进行更新，（D）使用20秒连续内部采样每分钟进行一次完整的ECM更新。
增强二次ECM3具有最佳的拟合精度，左右（LR）方向的平均（±标准差）RMSE为0.32±0.11毫米，头脚（CC）方向为0.79±0.30毫米，前后（AP）方向为0.56±0.31毫米。然而，更简单但结合频繁基线更新的增强线性ECM2（更新策略C）给出了最佳的运动估计，LR方向的平均RMSE为0.41±0.14毫米，CC方向为1.02±0.33毫米，AP方向为0.78±0.48毫米。对于CC方向的运动，这明显优于所有其他ECM更新策略组合（Wilcoxon符号秩检验p<0.05）。
增强线性ECM2结合频繁基线更新在拟合精度和对不规则运动的稳健性之间提供了最佳折衷。它通过将外部运动监测与传统直线加速器可用的稀疏0.33Hz千伏成像相结合，实现了准确的内部运动监测。
