School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
Department of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China.
Thorac Cancer. 2023 Nov;14(33):3266-3274. doi: 10.1111/1759-7714.15117. Epub 2023 Sep 24.
In view of the fact that radiomics features have been reported as predictors of immunotherapy to various cancers, this study aimed to develop a prediction model to determine the response to anti-programmed death-1 (anti-PD-1) therapy in esophageal squamous cell carcinoma (ESCC) patients from contrast-enhanced CT (CECT) radiomics features.
Radiomic analysis of images was performed retrospectively for image samples before and after anti-PD-1 treatment, and efficacy analysis was performed for the results of two different time node evaluations. A total of 68 image samples were included in this study. Quantitative radiomic features were extracted from the images, and the least absolute shrinkage and selection operator method was applied to select radiomic features. After obtaining selected features, three classification models were used to establish a radiomics model to predict the ESCC status and efficacy of therapy. A cross-validation strategy utilizing three folds was employed to train and test the model. Performance evaluation of the model was done using the area under the curve (AUC) of receiver operating characteristic, sensitivity, specificity, and precision metric.
Wavelet and area of gray level change (log-sigma) were the most significant radiomic features for predicting therapy efficacy. Fifteen radiomic features from the whole tumor and peritumoral regions were selected and comprised of the fusion radiomics score. A radiomics classification was developed with AUC of 0.82 and 0.884 in the before and after-therapy cohorts, respectively.
The combined model incorporating radiomic features and clinical CECT predictors helps to predict the response to anti-PD-1therapy in patients with ESCC.
鉴于放射组学特征已被报道为各种癌症免疫治疗的预测因子，本研究旨在从增强 CT（CECT）放射组学特征中开发一种预测模型，以确定食管鳞癌（ESCC）患者对抗程序性死亡-1（抗 PD-1）治疗的反应。
对接受抗 PD-1 治疗前后的图像样本进行回顾性放射组学分析，并对两种不同时间节点评估结果进行疗效分析。本研究共纳入 68 个图像样本。从图像中提取定量放射组学特征，并应用最小绝对收缩和选择算子方法选择放射组学特征。获得选定特征后，使用三种分类模型建立放射组学模型，以预测 ESCC 状态和治疗效果。采用三折交叉验证策略对模型进行训练和测试。使用受试者工作特征曲线（AUC）、敏感性、特异性和精度度量来评估模型的性能。
小波和灰度变化面积（对数西格玛）是预测治疗效果最显著的放射组学特征。从整个肿瘤和肿瘤周围区域选择了 15 个放射组学特征，组成融合放射组学评分。建立了放射组学分类模型，在前治疗和后治疗队列中的 AUC 分别为 0.82 和 0.884。
纳入放射组学特征和临床 CECT 预测因子的联合模型有助于预测 ESCC 患者对抗 PD-1 治疗的反应。
