Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China.
Department of Radiotherapy, Shantou Central Hospital, Shantou, Guangdong, China.
Radiat Oncol. 2020 Oct 29;15(1):249. doi: 10.1186/s13014-020-01692-3.
To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features.
Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions.
A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUC of Rad-score was 0.812 (95% CI 0.742-0.869, p < 0.001) in the training set and 0.744 (95% CI 0.632-0.851, p = 0.003) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with p values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95% CI 0.779-0.897) in the training set and 0.807 (95% CI 0.691-0.894) in the validation set. Delong test showed that the nomogram model was significantly superior to the clinical staging, with p < 0.001 in the training set and p = 0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%.
We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.
利用治疗前 CT 放射组学特征，开发并验证一个列线图模型，以预测食管鳞状细胞癌（ESCC）患者同步放化疗（CCRT）后的完全缓解（CR）。
回顾性收集 2013 年 1 月至 2015 年 12 月在汕头市中心医院诊断为 ESCC 并接受 CCRT 治疗的患者数据。经过连续筛选，将符合条件的患者纳入本研究并随机分为训练集和验证集。在训练集中，采用最小绝对收缩和选择算子（LASSO）与逻辑回归来选择放射组学特征并计算 Rad-score。通过逻辑回归分析确定预测列线图模型的临床预测因素。使用受试者工作特征曲线（ROC）下面积（AUC）评估预测列线图模型的性能，并使用决策曲线分析列线图模型对临床治疗决策的影响。
共纳入 226 例患者，并随机分为两组，训练集 160 例，验证集 66 例。经过 LASSO 分析，筛选出 7 个放射组学特征来建立放射组学特征 Rad-score。在训练集中，Rad-score 的 AUC 为 0.812（95%CI 0.742-0.869，p<0.001），在验证集中为 0.744（95%CI 0.632-0.851，p=0.003）。多变量分析显示，Rad-score 和临床分期是 CR 状态的独立预测因素，p 值分别为 0.035 和 0.023。建立并验证了一个包含 Rad-score 和临床分期的列线图模型，在训练集中的 AUC 为 0.844（95%CI 0.779-0.897），在验证集中为 0.807（95%CI 0.691-0.894）。Delong 检验表明，该列线图模型明显优于临床分期，在训练集中 p<0.001，在验证集中 p=0.026。决策曲线显示，当风险阈值大于 25%时，列线图模型优于临床分期。
我们开发并验证了一个用于预测 ESCC 患者 CCRT 后 CR 状态的列线图模型。该模型结合了放射组学特征 Rad-score 和临床分期。该模型为我们提供了一种经济、简便的方法来评估 ESCC 患者放化疗的反应。
