目的:研究从物理剂量(DPHYS)中提取的剂量组学特征的作用,RBE加权剂量(DRBE)和剂量平均线性能量转移(LETd),预测碳离子放疗(CIRT)治疗颅底脊索瘤(SBC)局部复发(LR)的风险。因此,定义和评估剂组学驱动的肿瘤控制概率(TCP)模型。
方法:回顾性选择54例SBC患者进行本研究。在重复的交叉验证(CV)中调整了正则化的Cox比例风险模型(r-Cox)和生存支持向量机(s-SVM),并且在LR的低/高风险中对患者进行分层。通过Harrell的一致性统计(C指数)评估模型的性能,生存率通过Kaplan-Meier(KM)曲线表示。将多变量逻辑回归拟合到所选择的特征集,以生成用于每个图的剂量组学驱动的TCP模型。在f评分和准确性方面,将这些与使用临床参数构建的参考模型进行比较。
结果:使用r-Cox和s-SVM,LETd图达到了0.750和0.786的测试C指数,并显著分离KM曲线。DPHYS图和临床参数显示有希望的CV结果,C指数高于0.8,尽管在测试集和患者分层方面表现较差。基于LETd的TCP显示出显着更高的f分数(0.67[0.52-0.70],中位数[IQR])与临床模型(0.4[0.32-0.63],p<0.025),而DPHYS实现了显著更高的精度(DPHYS:0.73[0.65-0.79],临床:0.6[0.52-0.72])。
结论:该分析支持LETd作为接受CIRT治疗的SBC中LR预后因素的相关来源。这反映在TCP建模中,其中LETd和DPHYS相对于临床模型显示出改善的性能。
OBJECTIVE: To investigate the role of dosiomics features extracted from physical dose (DPHYS), RBE-weighted dose (DRBE) and dose-averaged Linear Energy Transfer (LETd), to predict the risk of local recurrence (LR) in skull base chordoma (SBC) treated with Carbon Ion Radiotherapy (CIRT). Thus, define and evaluate dosiomics-driven tumor control probability (TCP) models.
METHODS: 54 SBC patients were retrospectively selected for this study. A regularized Cox proportional hazard model (r-Cox) and Survival Support Vector Machine (s-SVM) were tuned within a repeated Cross Validation (CV) and patients were stratified in low/high risk of LR. Models\' performance was evaluated through Harrell\'s concordance statistic (C-index), and survival was represented through Kaplan-Meier (KM) curves. A multivariable logistic regression was fit to the selected feature sets to generate a dosiomics-driven TCP model for each map. These were compared to a reference model built with clinical parameters in terms of f-score and accuracy.
RESULTS: The LETd maps reached a test C-index of 0.750 and 0.786 with r-Cox and s-SVM, and significantly separated KM curves. DPHYS maps and clinical parameters showed promising CV outcomes with C-index above 0.8, despite a poorer performance on the test set and patients stratification. The LETd-based TCP showed a significatively higher f-score (0.67[0.52-0.70], median[IQR]) compared to the clinical model (0.4[0.32-0.63], p < 0.025), while DPHYS achieved a significatively higher accuracy (DPHYS: 0.73[0.65-0.79], Clinical: 0.6 [0.52-0.72]).
CONCLUSIONS: This analysis supports the role of LETd as relevant source of prognostic factors for LR in SBC treated with CIRT. This is reflected in the TCP modeling, where LETd and DPHYS showed an improved performance with respect to clinical models.