Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan; School of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Radiother Oncol. 2023 Dec;189:109938. doi: 10.1016/j.radonc.2023.109938. Epub 2023 Oct 6.
We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC).
A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated.
The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia.
Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.
我们旨在探讨喉癌和下咽癌(LHC)患者放疗(RT)过程中基于肿瘤周围和肿瘤内 CT 的放射组学的预后价值。
共纳入 92 例符合条件的患者,按 1:1 比例随机分为训练集和验证集。从治疗前和中期 CT 中提取放射组学特征。LASSO-Cox 回归用于特征选择和模型构建。时间依赖性接受者操作特征曲线(AUC)分析用于评估模型的预后性能。Kaplan-Meier 法和 Cox 回归用于评估整体生存(OS)和无进展生存(PFS)的风险分层能力。还评估了放射组学与临床参数和循环淋巴细胞计数之间的相关性。
在验证组中,与治疗前肿瘤内模型(AUC:0.62)相比,中期肿瘤周围(AUC:0.77)和肿瘤内(AUC:0.79)放射组学模型对 OS 的预测性能更好。Kaplan-Meier 分析证实了这一点,其中风险分层取决于中期肿瘤周围(p=0.009)和肿瘤内(p=0.003)放射组学,与治疗前肿瘤内放射组学(p=0.199)相比,OS 风险分层可以得到改善。多变量分析确定中期肿瘤周围和肿瘤内放射组学模型是 OS 和 PFS 的独立预后因素。中期肿瘤周围和肿瘤内放射组学与治疗相关的淋巴细胞减少相关。
中期肿瘤周围和肿瘤内放射组学模型是有前途的影像生物标志物,可用于预测 LHC 患者接受 RT 治疗的 OS 和 PFS。