Radiomics and dosiomics-based prediction of radiotherapy-induced xerostomia in head and neck cancer patients

Dose-response modeling for radiotherapy-induced xerostomia in head and neck cancer (HN) patients is a promising frontier for personalized therapy. Feature extraction from diagnostic and therapeutic images (radiomics and dosiomics features) can be used for data-driven response modeling. The aim of this study is to develop xerostomia predictive models based on radiomics-dosiomics features. Data from the cancer imaging archive (TCIA) for 31 HN cancer patients were employed. For all patients, parotid CT radiomics features were extracted, utilizing Lasso regression for feature selection and multivariate modeling. The models were developed by selected features from pretreatment (CT₁), mid-treatment (CT₂), post-treatment (CT₃), and delta features (ΔCT_2-1, ΔCT_3-1, ΔCT_3-2). We also considered dosiomics features extracted from the parotid dose distribution images (Dose model). Thus, combination models of radio-dosiomics (CT + dose & ΔCT + dose) were developed. Moreover, clinical, and dose-volume histogram (DVH) models were built. Nested 10-fold cross-validation was used to assess the predictive classification of patients into those with and without xerostomia, and the area under the receiver operative characteristic curve (AUC) was used to compare the predictive power of the models. The sensitivity and accuracy of models also were obtained. In total, 59 parotids were assessed, and 13 models were developed. Our results showed three models with AUC of 0.89 as most predictive, namely ΔCT_2-1 + Dose (Sensitivity 0.99, Accuracy 0.94 & Specificity 0.86), CT₃ model (Sensitivity 0.96, Accuracy 0.94 & Specificity 0.86) and DVH (Sensitivity 0.93, Accuracy 0.89 & Specificity 0.84). These models were followed by Clinical (AUC 0.89, Sensitivity 0.81, Accuracy 0.97 & Specificity 0.89) and CT₂ & Dose (AUC 0.86, Sensitivity 0.97, Accuracy 0.87 & Specificity 0.82). The Dose model (developed by dosiomics features only) had AUC, Sensitivity, Specificity, and Accuracy of 0.72, 0.98, 0.33, and 0.79 respectively. Quantitative features extracted from diagnostic imaging during and after radiotherapy alone or in combination with dosiomics markers obtained from dose distribution images can be used for radiotherapy response modeling, opening up prospects for personalization of therapies toward improved therapeutic outcomes.

针对头颈癌（Head and Neck Cancer, HN）患者放疗诱导口干症的剂量响应建模，是面向个性化治疗的极具潜力的前沿研究方向。从诊断与治疗影像中提取的放射组学特征与剂量组学特征，可用于构建数据驱动的响应模型。本研究旨在基于放射组学-剂量组学特征构建口干症预测模型。本研究使用了癌症影像档案（The Cancer Imaging Archive, TCIA）中31例头颈癌患者的数据集。针对所有患者，研究人员提取了腮腺CT放射组学特征，并采用Lasso回归进行特征选择与多变量建模。模型构建所选用的特征来源于治疗前（CT₁）、治疗中（CT₂）、治疗后（CT₃）的影像特征，以及差值特征（ΔCT₂₋₁、ΔCT₃₋₁、ΔCT₃₋₂）。此外，本研究还纳入了从腮腺剂量分布影像中提取的剂量组学特征（即剂量模型），并构建了放射组学-剂量组学联合模型（CT+剂量与ΔCT+剂量）。同时，本研究还搭建了临床模型与剂量体积直方图（Dose-Volume Histogram, DVH）模型。研究采用嵌套10折交叉验证来评估模型对口干症患者与非口干症患者的分类预测性能，并以受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve, AUC）对比各模型的预测能力，同时获取了各模型的灵敏度与准确率。本研究共评估了59个腮腺样本，构建了13个预测模型。结果显示，3个模型的AUC达到0.89，表现最优：分别为ΔCT₂₋₁+剂量模型（灵敏度0.99、准确率0.94、特异度0.86）、CT₃模型（灵敏度0.96、准确率0.94、特异度0.86）以及DVH模型（灵敏度0.93、准确率0.89、特异度0.84）。紧随其后的是临床模型（AUC 0.89、灵敏度0.81、准确率0.97、特异度0.89）与CT₂+剂量模型（AUC 0.86、灵敏度0.97、准确率0.87、特异度0.82）。仅采用剂量组学特征构建的剂量模型，其AUC、灵敏度、特异度与准确率分别为0.72、0.98、0.33与0.79。综上，从放疗期间及放疗后诊断影像中提取的定量特征，单独或与剂量分布影像得到的剂量组学标志物联合使用，可用于放疗响应建模，为实现个性化治疗以改善治疗结局开辟了新的前景。