PDF(Pubmed)
Abstract：
OBJECTIVE: Development and validation of a radiomics model for predicting occult locally advanced esophageal squamous cell carcinoma (LA-ESCC) on computed tomography (CT) radiomic features before implementation of treatment.
METHODS: The study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity.
RESULTS: A total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT1-2N0M0 diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort.
CONCLUSIONS: The radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.
METHODS: The study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity.
RESULTS: A total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT1-2N0M0 diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort.
CONCLUSIONS: The radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.
摘要：
目的：开发并验证一种影像组学模型，用于在实施治疗前预测隐匿性局部晚期食管鳞状细胞癌（LA-ESCC）的计算机断层扫描（CT）影像特征。
方法：该研究回顾性收集了来自两个医疗中心的574例食管鳞状细胞癌（ESCC）患者，分为三组进行培训，内部和外部验证。在描绘感兴趣的体积(VOI)之后，使用三种稳健方法提取影像组学特征并进行特征选择。随后，构建了10个机器学习模型，其中，利用最佳模型建立了影像组学签名。此外，我们开发了一个结合了临床和影像组学特征的预测性列线图.通过接收器工作特性曲线评估了这些模型的性能，校正曲线,决策曲线分析以及包括准确性在内的措施，灵敏度,和特异性。
结果：总共选择了19个影像组学特征。多层感知器（MLP），被发现是最优的，在训练中达到0.919、0.864和0.882的AUC，内部和外部验证队列，分别。同样，MLP在区分cT1-2N0M0亚组中隐匿性LA-ESCC方面显示出良好的准确性，在两个验证队列中分别为0.803和0.789。通过将影像组学签名与临床签名相结合，在外部验证队列中,预测列线图显示出优异的预测性能,AUC为0.877,准确度为0.85.
结论：影像组学和机器学习模型可以提高隐匿性LA-ESCC预测的准确性，为临床医生选择治疗方案提供有价值的帮助。
方法：该研究回顾性收集了来自两个医疗中心的574例食管鳞状细胞癌（ESCC）患者，分为三组进行培训，内部和外部验证。在描绘感兴趣的体积(VOI)之后，使用三种稳健方法提取影像组学特征并进行特征选择。随后，构建了10个机器学习模型，其中，利用最佳模型建立了影像组学签名。此外，我们开发了一个结合了临床和影像组学特征的预测性列线图.通过接收器工作特性曲线评估了这些模型的性能，校正曲线,决策曲线分析以及包括准确性在内的措施，灵敏度,和特异性。
结果：总共选择了19个影像组学特征。多层感知器（MLP），被发现是最优的，在训练中达到0.919、0.864和0.882的AUC，内部和外部验证队列，分别。同样，MLP在区分cT1-2N0M0亚组中隐匿性LA-ESCC方面显示出良好的准确性，在两个验证队列中分别为0.803和0.789。通过将影像组学签名与临床签名相结合，在外部验证队列中,预测列线图显示出优异的预测性能,AUC为0.877,准确度为0.85.
结论：影像组学和机器学习模型可以提高隐匿性LA-ESCC预测的准确性，为临床医生选择治疗方案提供有价值的帮助。
