背景:MR引导的放射治疗(MRgRT)系统比基于X射线的系统提供更好的软组织对比度,并且可以获取实时电影以进行治疗门控。这些功能可以减少治疗计划的裕度,允许改善关键结构节约和降低治疗毒性。尽管有这种改善,泌尿生殖系统(GU)毒性继续影响许多患者。
目的:(1)确定剂量学预测因子,可能与临床参数相结合,通过利用MRgRT准确监测每日剂量,了解SBRT后GU毒性,超出计划期间计算的预测剂量。(2)提高对毒性敏感的膀胱亚结构的认识,特别是三角区和尿道.
方法:69名前列腺癌患者(NCT04384770临床试验)接受了ViewRayMRIdianMRgRT系统治疗,在五个以上的部分中,对95%的PTV规定了40Gy。总的来说,17例(24.6%)前列腺患者报告急性2级GU毒性。CTV,PTV,膀胱,膀胱壁,三角,尿道,直肠,在计划和日常治疗MRI上对直肠壁进行轮廓分析。计划和每日治疗DVH(0.1Gy增量),器官剂量(min,max,mean),并记录器官体积。通过基于每日设置对齐将计划剂量分布转移到每日MRI来估计每日剂量。将患者分为训练集(55)和测试集(14)。使用t检验和随后的最大相关性最小冗余度(MRMR)算法对剂量特征进行预过滤。使用正则化方法对Logistic回归进行研究,以选择剂量学预测因子。具体来说,两种方法:时间组最小绝对收缩和选择(LASSO),研究了交互式分组贪婪算法(IGA)。将计划中的共享特征和五个治疗部分分组以鼓励一致性和稳定性。还评估了常规平坦的非时间分组的LASSO以提供坚实的基准。选择功能后,最后训练了逻辑回归模型.剂量学回归模型与仅具有临床参数(年龄,基线IPSS,前列腺大小,ADT用法,等。)和混合模型,将最佳表现的剂量测定特征与临床参数相结合,进行了评估。使用准确性在测试集上评估最终模型性能,灵敏度,和特异性由训练集的最佳阈值确定。
结果:IGA具有最佳的测试性能,准确性/敏感性/特异性为0.79/0.67/0.82,选择了覆盖膀胱的12组(V19.8Gy,V20.5Gy),膀胱壁(19.7Gy),三角(15.9,18.2,43.3Gy),尿道(V41.4Gy,V41.7Gy),CTV(V41.9Gy),直肠(V8.5Gy),和直肠壁(1.2,44.1Gy)剂量特征。绝对膀胱V19.8Gy和V20.5Gy是最重要的特征,其次是相对三角15.9和18.2Gy。在具有IGA的混合模型中包含临床参数并没有显着改变回归性能。
结论:总体而言,IGA特征选择导致最佳的GU毒性预测性能。这项探索性研究证明了识别和分析剂量学毒性预测因子的可行性,并意识到敏感的子结构和每日剂量,以潜在地提供一致和稳定的剂量学指标来指导治疗计划。需要进一步的患者累积以进一步评估剂量学预测因子并进行验证。
BACKGROUND: MR-guided radiation therapy (MRgRT) systems provide superior soft tissue contrast than x-ray based systems and can acquire real-time cine for treatment gating. These features allow treatment planning margins to be reduced, allowing for improved critical structure sparing and reduced treatment toxicity. Despite this improvement, genitourinary (GU) toxicity continues to affect many patients.
OBJECTIVE: (1) To identify dosimetric predictors, potentially in combination with clinical parameters, of GU toxicity following SBRT by leveraging MRgRT to accurately monitor daily dose, beyond predicted dose calculated during planning. (2) Improve awareness of toxicity-sensitive bladder substructures, specifically the
trigone and urethra.
METHODS: Sixty-nine prostate cancer patients (NCT04384770 clinical trial) were treated on a ViewRay MRIdian MRgRT system, with 40 Gy prescribed to 95% of the PTV in over five fractions. Overall, 17 (24.6%) prostate patients reported acute grade 2 GU toxicity. The CTV, PTV, bladder, bladder wall,
trigone, urethra, rectum, and rectal wall were contoured on the planning and daily treatment MRIs. Planning and daily treatment DVHs (0.1 Gy increments), organ doses (min, max, mean), and organ volumes were recorded. Daily dose was estimated by transferring the planning dose distributions to the daily MRI based on the daily setup alignment. Patients were partitioned into a training (55) and testing set (14). Dose features were pre-filtered using a t-test followed by maximum relevance minimum redundancy (MRMR) algorithm. Logistic regression was investigated with regularization to select dosimetric predictors. Specifically, two approaches: time-group least absolute shrinkage and selection (LASSO), and interactive grouped greedy algorithm (IGA) were investigated. Shared features across the planning and five treatment fractions were grouped to encourage consistency and stability. The conventional flat non-temporally grouped LASSO was also evaluated to provide a solid benchmark. After feature selection, a final logistic regression model was trained. Dosimetric regression models were compared to a clinical regression model with only clinical parameters (age, baseline IPSS, prostate gland size, ADT usage, etc.) and a hybrid model, combining the best performing dosimetric features with the clinical parameters, was evaluated. Final model performance was evaluated on the testing set using accuracy, sensitivity, and specificity determined by the optimal threshold of the training set.
RESULTS: IGA had the best testing performance with an accuracy/sensitivity/specificity of 0.79/0.67/0.82, selecting 12 groups covering the bladder (V19.8 Gy, V20.5 Gy), bladder wall (19.7 Gy),
trigone (15.9, 18.2, 43.3 Gy), urethra (V41.4 Gy, V41.7 Gy), CTV (V41.9 Gy), rectum (V8.5 Gy), and rectal wall (1.2, 44.1 Gy) dose features. Absolute bladder V19.8 Gy and V20.5 Gy were the most important features, followed by relative
trigone 15.9 and 18.2 Gy. Inclusion of clinical parameters in the hybrid model with IGA did not significantly change regression performance.
CONCLUSIONS: Overall, IGA feature selection resulted in the best GU toxicity prediction performance. This exploratory study demonstrated the feasibility of identification and analysis of dosimetric toxicity predictors with awareness to sensitive substructures and daily dose to potentially provide consistent and stable dosimetric metrics to guide treatment planning. Further patient accruement is warranted to further assess dosimetric predictor and perform validation.