PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to investigate the ability of enhanced computed tomography (CT)-based radiomics and dosimetric parameters in predicting response to radiotherapy for esophageal cancer.
UNASSIGNED: A retrospective analysis of 147 patients diagnosed with esophageal cancer was performed, and the patients were divided into a training group (104 patients) and a validation group (43 patients). In total, 851 radiomics features were extracted from the primary lesions for analysis. Maximum correlation minimum redundancy and minimum least absolute shrinkage and selection operator were utilized for feature screening of radiomics features, and logistic regression was applied to construct a radiotherapy radiomics model for esophageal cancer. Finally, univariate and multivariate parameters were used to identify significant clinical and dosimetric characteristics for constructing combination models. The area evaluated the predictive performance under the receiver operating characteristics (AUC) curve and the accuracy, sensitivity, and specificity of the training and validation cohorts.
UNASSIGNED: Univariate logistic regression analysis revealed statistically significant differences in clinical parameters of sex (p=0.031) and esophageal cancer thickness (p=0.028) on treatment response, whereas dosimetric parameters did not differ significantly in response to treatment. The combined model demonstrated improved discrimination between the training and validation groups, with AUCs of 0.78 (95% confidence interval [CI], 0.69-0.87) and 0.79 (95% CI, 0.65-0.93) in the training and validation groups, respectively.
UNASSIGNED: The combined model has potential application value in predicting the treatment response of patients with esophageal cancer after radiotherapy.
UNASSIGNED: A retrospective analysis of 147 patients diagnosed with esophageal cancer was performed, and the patients were divided into a training group (104 patients) and a validation group (43 patients). In total, 851 radiomics features were extracted from the primary lesions for analysis. Maximum correlation minimum redundancy and minimum least absolute shrinkage and selection operator were utilized for feature screening of radiomics features, and logistic regression was applied to construct a radiotherapy radiomics model for esophageal cancer. Finally, univariate and multivariate parameters were used to identify significant clinical and dosimetric characteristics for constructing combination models. The area evaluated the predictive performance under the receiver operating characteristics (AUC) curve and the accuracy, sensitivity, and specificity of the training and validation cohorts.
UNASSIGNED: Univariate logistic regression analysis revealed statistically significant differences in clinical parameters of sex (p=0.031) and esophageal cancer thickness (p=0.028) on treatment response, whereas dosimetric parameters did not differ significantly in response to treatment. The combined model demonstrated improved discrimination between the training and validation groups, with AUCs of 0.78 (95% confidence interval [CI], 0.69-0.87) and 0.79 (95% CI, 0.65-0.93) in the training and validation groups, respectively.
UNASSIGNED: The combined model has potential application value in predicting the treatment response of patients with esophageal cancer after radiotherapy.
摘要：
■本研究旨在研究基于增强计算机断层扫描（CT）的放射组学和剂量学参数在预测食管癌放疗反应中的能力。
■对147例诊断为食管癌的患者进行了回顾性分析，将患者分为训练组(104例)和验证组(43例).总的来说,从原发性病变中提取851个影像组学特征用于分析。最大相关最小冗余和最小最小绝对收缩和选择运算符用于影像组学特征的特征筛选。并应用逻辑回归方法构建食管癌放疗影像组学模型。最后,使用单变量和多变量参数来确定显著的临床和剂量学特征,以构建组合模型.面积评估了接收器工作特性（AUC）曲线下的预测性能和准确性，灵敏度,以及培训和验证队列的特异性。
■单变量logistic回归分析显示，性别（p=0.031）和食管癌厚度（p=0.028）的临床参数在治疗反应方面具有统计学意义。而剂量学参数对治疗的反应没有显着差异。组合模型证明了训练组和验证组之间的区别得到了改善，AUC为0.78(95%置信区间[CI]，0.69-0.87）和0.79（95%CI，0.65-0.93）在训练和验证组中，分别。
■组合模型在预测食管癌患者放疗后的治疗反应方面具有潜在的应用价值。
■对147例诊断为食管癌的患者进行了回顾性分析，将患者分为训练组(104例)和验证组(43例).总的来说,从原发性病变中提取851个影像组学特征用于分析。最大相关最小冗余和最小最小绝对收缩和选择运算符用于影像组学特征的特征筛选。并应用逻辑回归方法构建食管癌放疗影像组学模型。最后,使用单变量和多变量参数来确定显著的临床和剂量学特征,以构建组合模型.面积评估了接收器工作特性（AUC）曲线下的预测性能和准确性，灵敏度,以及培训和验证队列的特异性。
■单变量logistic回归分析显示，性别（p=0.031）和食管癌厚度（p=0.028）的临床参数在治疗反应方面具有统计学意义。而剂量学参数对治疗的反应没有显着差异。组合模型证明了训练组和验证组之间的区别得到了改善，AUC为0.78(95%置信区间[CI]，0.69-0.87）和0.79（95%CI，0.65-0.93）在训练和验证组中，分别。
■组合模型在预测食管癌患者放疗后的治疗反应方面具有潜在的应用价值。
