Table_1_A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma.XLSX

To construct a prognostic model for preoperative prediction on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC), we created radiomics signature with high throughput radiomics features extracted from CT images of 272 patients (204 in training and 68 in validation cohort). Multivariable logistic regression was applied to build the radiomics signature and the predictive nomogram model, which was composed of radiomics signature, traditional TNM stage, and clinical features. A total of 21 radiomics features were selected from 954 to build a radiomics signature which was significantly associated with progression-free survival (p < 0.001). The area under the curve of performance was 0.878 (95% CI: 0.831–0.924) for the training cohort and 0.857 (95% CI: 0.767–0.947) for the validation cohort. The radscore of signatures' combination showed significant discrimination for survival status. Radiomics nomogram combined radscore with TNM staging and showed considerable improvement over TNM staging alone in the training cohort (C-index, 0.770 vs. 0.603; p < 0.05), and it is the same with clinical data (C-index, 0.792 vs. 0.680; p < 0.05), which were confirmed in the validation cohort. Decision curve analysis showed that the model would receive a benefit when the threshold probability was between 0 and 0.9. Collectively, multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

为构建用于食管鳞状细胞癌（esophageal squamous cell carcinoma, ESCC）术前计算机断层扫描（computed tomography, CT）影像预测的预后模型，本研究构建了放射组学特征签名：从272例患者的CT影像中提取高通量放射组学特征，其中训练队列纳入204例患者，验证队列纳入68例患者。采用多变量逻辑回归分析构建放射组学特征签名，以及由放射组学签名、传统TNM分期与临床特征共同组成的预测性列线图模型。本研究从954个放射组学特征中筛选出21个用于构建放射组学特征签名，该签名与无进展生存期显著相关（p < 0.001）。训练队列的模型曲线下面积（AUC）为0.878（95%置信区间：0.831–0.924），验证队列的曲线下面积为0.857（95%置信区间：0.767–0.947）。联合签名的放射组学评分（radscore）对生存状态具有显著区分能力。将放射组学评分与TNM分期结合的放射组学列线图，在训练队列中相较单纯TNM分期展现出显著性能提升（一致性指数C-index：0.770 vs. 0.603；p < 0.05）；联合临床数据后同样实现性能显著提升（C-index：0.792 vs. 0.680；p < 0.05），该结果在验证队列中得到验证。决策曲线分析显示，当阈值概率介于0至0.9之间时，使用该模型可使患者获益。综上，基于多参数CT的放射组学列线图可有效提升食管鳞状细胞癌的预后预测能力。