Ou Jing, Zhou Hai-Ying, Qin Hui-Lin, Wang Yue-Su, Gou Yue-Qin, Luo Hui, Zhang Xiao-Ming, Chen Tian-Wu
The First Clinical College of Jinan University, and Jinan University First Affiliated Hospital, Guangzhou, Guangdong 510630, China; Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China.
Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China.
Eur J Radiol. 2024 Dec;181:111763. doi: 10.1016/j.ejrad.2024.111763. Epub 2024 Sep 26.
To develop a CT radiomics model to predict pathological complete response (pCR) of advanced esophageal squamous cell carcinoma (ESCC) toneoadjuvant chemotherapy using paclitaxel and cisplatin.
326 consecutive patients with advanced ESCC from two hospitals undergoing baseline contrast-enhanced CT followed by neoadjuvant chemotherapy using paclitaxel and cisplatin were enrolled, including 115 patients achieving pCR and 211 patients without pCR. Of the 271 cases from 1st hospital, 188 and 83 cases were randomly allocated to the training and test cohorts, respectively. The 55 patients from a second hospital were assigned as an external validation cohort. Region of interest was segmented on the baseline thoracic contrast-enhanced CT. Useful radiomics features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomics features were chosen using support vector machine (SVM). Discriminating performance was assessed with area under the receiver operating characteristic curve (ROC) and F-1score. The calibration curves and Brier score were used to evaluate the predictive accuracy.
Eight radiomics features were selected to create radiomics models related to pCR of advanced ESCC (P-values < 0.01 for both the training and test cohorts). SVM model showed the best performance (AUCs = 0.929, 0.868 and 0.866, F-1scores = 0.857, 0.847 and 0.737 in the training, test and external validation cohorts, respectively). The calibration curves and Brier scores indicated goodness-of-fit and its great predictive accuracy.
CT radiomics models could well help predict pCR of advanced ESCC, and SVM model could be a suitable predictive model.
建立一种CT影像组学模型，用于预测晚期食管鳞状细胞癌（ESCC）患者接受紫杉醇和顺铂新辅助化疗后的病理完全缓解（pCR）情况。
连续纳入两家医院的326例晚期ESCC患者，这些患者均接受了基线增强CT检查，随后接受紫杉醇和顺铂新辅助化疗，其中115例患者达到pCR，211例患者未达到pCR。在第一家医院的271例病例中，188例和83例分别随机分配至训练组和测试组。来自第二家医院的55例患者被指定为外部验证组。在基线胸部增强CT上勾画感兴趣区。使用最小绝对收缩和选择算子进行降维，生成有用的影像组学特征。使用支持向量机（SVM）选择最佳影像组学特征。通过受试者工作特征曲线（ROC）下面积和F-1评分评估鉴别性能。校准曲线和Brier评分用于评估预测准确性。
选择了8个影像组学特征来建立与晚期ESCC的pCR相关的影像组学模型（训练组和测试组的P值均<0.01）。SVM模型表现最佳（训练组、测试组和外部验证组的AUC分别为0.929、0.868和0.866，F-1评分分别为0.857、0.847和0.737）。校准曲线和Brier评分表明模型拟合良好且预测准确性高。
CT影像组学模型能够很好地帮助预测晚期ESCC的pCR，SVM模型可能是一种合适的预测模型。
