An Dianzheng, Cao Qiang, Su Na, Li Wanhu, Li Zhe, Liu Yanxiao, Zhang Yuxing, Li Baosheng
Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong University, Jinan, China.
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Front Oncol. 2022 Mar 15;12:787489. doi: 10.3389/fonc.2022.787489. eCollection 2022.
The purpose of this study was to investigate the association between the radiomics features (RFs) extracted from a whole-tumor ADC map during the early treatment course and response to concurrent chemoradiotherapy (cCRT) in patients with esophageal squamous cell carcinoma (ESCC).
Patients with ESCC who received concurrent chemoradiotherapy were enrolled in two hospitals. Whole-tumor ADC values and RFs were extracted from sequential ADC maps before treatment, after the 5th radiation, and after the 10th radiation, and the changes of ADC values and RFs were calculated as the relative difference between different time points. RFs were selected and further imported to a support vector machine classifier for building a radiomics signature. Radiomics signatures were obtained from both RFs extracted from pretreatment images and three sets of delta-RFs. Prediction models for different responders based on clinical characteristics and radiomics signatures were built up with logistic regression.
Patients (n=76) from hospital 1 were randomly assigned to training (n=53) and internal testing set (n=23) in a ratio of 7 to 3. In addition, to further test the performance of the model, data from another institute (n=17) were assigned to the external testing set. Neither ADC values nor delta-ADC values were correlated with treatment response in the three sets. It showed a predictive effect to treatment response that the AUC values of the radiomics signature built from delta-RFs over the first 2 weeks were 0.824, 0.744, and 0.742 in the training, the internal testing, and the external testing set, respectively. Compared with the evaluated response, the performance of response prediction in the internal testing set was acceptable ( = 0.048).
The ADC map-based delta-RFs during the early course of treatment were effective to predict the response to cCRT in patients with ESCC.
本研究旨在探讨在食管鳞状细胞癌（ESCC）患者早期治疗过程中，从全肿瘤表观扩散系数（ADC）图提取的影像组学特征（RFs）与同步放化疗（cCRT）疗效之间的关联。
两所医院招募了接受同步放化疗的ESCC患者。在治疗前、第5次放疗后和第10次放疗后，从连续的ADC图中提取全肿瘤ADC值和RFs，并将ADC值和RFs的变化计算为不同时间点之间的相对差异。选择RFs并进一步导入支持向量机分类器以构建影像组学特征。从预处理图像提取的RFs和三组增量RFs中均获得了影像组学特征。基于临床特征和影像组学特征，使用逻辑回归建立不同反应者的预测模型。
医院1的患者（n = 76）以7:3的比例随机分配到训练组（n = 53）和内部测试组（n = 23）。此外，为进一步测试模型的性能，将来自另一机构的数据（n = 17）分配到外部测试组。在这三组中，ADC值和增量ADC值均与治疗反应无关。在前2周由增量RFs构建的影像组学特征的曲线下面积（AUC）值在训练组、内部测试组和外部测试组中分别为0.824、0.744和0.742，显示出对治疗反应的预测作用。与评估的反应相比，内部测试组中反应预测的性能是可接受的（P = 0.048）。
治疗早期基于ADC图的增量RFs可有效预测ESCC患者对cCRT的反应。
