PDF(Pubmed)
Abstract：
UNASSIGNED: Noninvasively detecting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients before targeted therapy remains a challenge. This study aimed to develop a 3-dimensional (3D) convolutional neural network (CNN)-based deep learning model to predict EGFR mutation status using computed tomography (CT) images.
UNASSIGNED: We retrospectively collected 660 patients from 2 large medical centers. The patients were divided into training (n=528) and external test (n=132) sets according to hospital source. The CNN model was trained in a supervised end-to-end manner, and its performance was evaluated using an external test set. To compare the performance of the CNN model, we constructed 1 clinical and 3 radiomics models. Furthermore, we constructed a comprehensive model combining the highest-performing radiomics and CNN models. The receiver operating characteristic (ROC) curves were used as primary measures of performance for each model. Delong test was used to compare performance differences between different models.
UNASSIGNED: Compared with the clinical [training set, area under the curve (AUC) =69.6%, 95% confidence interval (CI), 0.661-0.732; test set, AUC =68.4%, 95% CI, 0.609-0.752] and the highest-performing radiomics models (training set, AUC =84.3%, 95% CI, 0.812-0.873; test set, AUC =72.4%, 95% CI, 0.653-0.794) models, the CNN model (training set, AUC =94.3%, 95% CI, 0.920-0.961; test set, AUC =94.7%, 95% CI, 0.894-0.978) had significantly better predictive performance for predicting EGFR mutation status. In addition, compared with the comprehensive model (training set, AUC =95.7%, 95% CI, 0.942-0.971; test set, AUC =87.4%, 95% CI, 0.820-0.924), the CNN model had better stability.
UNASSIGNED: The CNN model has excellent performance in non-invasively predicting EGFR mutation status in patients with lung adenocarcinoma and is expected to become an auxiliary tool for clinicians.
UNASSIGNED: We retrospectively collected 660 patients from 2 large medical centers. The patients were divided into training (n=528) and external test (n=132) sets according to hospital source. The CNN model was trained in a supervised end-to-end manner, and its performance was evaluated using an external test set. To compare the performance of the CNN model, we constructed 1 clinical and 3 radiomics models. Furthermore, we constructed a comprehensive model combining the highest-performing radiomics and CNN models. The receiver operating characteristic (ROC) curves were used as primary measures of performance for each model. Delong test was used to compare performance differences between different models.
UNASSIGNED: Compared with the clinical [training set, area under the curve (AUC) =69.6%, 95% confidence interval (CI), 0.661-0.732; test set, AUC =68.4%, 95% CI, 0.609-0.752] and the highest-performing radiomics models (training set, AUC =84.3%, 95% CI, 0.812-0.873; test set, AUC =72.4%, 95% CI, 0.653-0.794) models, the CNN model (training set, AUC =94.3%, 95% CI, 0.920-0.961; test set, AUC =94.7%, 95% CI, 0.894-0.978) had significantly better predictive performance for predicting EGFR mutation status. In addition, compared with the comprehensive model (training set, AUC =95.7%, 95% CI, 0.942-0.971; test set, AUC =87.4%, 95% CI, 0.820-0.924), the CNN model had better stability.
UNASSIGNED: The CNN model has excellent performance in non-invasively predicting EGFR mutation status in patients with lung adenocarcinoma and is expected to become an auxiliary tool for clinicians.
摘要：
■在靶向治疗前非侵入性检测肺腺癌患者的表皮生长因子受体（EGFR）突变状态仍然是一个挑战。这项研究旨在开发基于3维（3D）卷积神经网络（CNN）的深度学习模型，以使用计算机断层扫描（CT）图像预测EGFR突变状态。
■我们回顾性地从2个大型医疗中心收集了660名患者。根据医院来源将患者分为训练（n=528）和外部测试（n=132）组。CNN模型是以有监督的端到端方式训练的，并使用外部测试集评估其性能。为了比较CNN模型的性能，我们构建了1个临床和3个影像组学模型.此外，我们构建了一个综合模型,该模型结合了性能最高的影像组学和CNN模型.接收器工作特性（ROC）曲线用作每个模型的性能的主要量度。Delong测试用于比较不同模型之间的性能差异。
■与临床[训练集相比，曲线下面积(AUC)=69.6%,95%置信区间(CI)，0.661-0.732;试验装置,AUC=68.4%，95%CI，0.609-0.752]和性能最高的影像组学模型(训练集，AUC=84.3%，95%CI，0.812-0.873；测试集，AUC=72.4%，95%CI，0.653-0.794)模型，CNN模型(训练集，AUC=94.3%，95%CI，0.920-0.961；测试集，AUC=94.7%，95%CI，0.894-0.978)对预测EGFR突变状态具有显著更好的预测性能。此外,与综合模型(训练集，AUC=95.7%，95%CI，0.942-0.971；测试集，AUC=87.4%，95%CI，0.820-0.924），CNN模型具有较好的稳定性。
■CNN模型在非侵入性预测肺腺癌患者的EGFR突变状态方面具有出色的性能，有望成为临床医生的辅助工具。
■我们回顾性地从2个大型医疗中心收集了660名患者。根据医院来源将患者分为训练（n=528）和外部测试（n=132）组。CNN模型是以有监督的端到端方式训练的，并使用外部测试集评估其性能。为了比较CNN模型的性能，我们构建了1个临床和3个影像组学模型.此外，我们构建了一个综合模型,该模型结合了性能最高的影像组学和CNN模型.接收器工作特性（ROC）曲线用作每个模型的性能的主要量度。Delong测试用于比较不同模型之间的性能差异。
■与临床[训练集相比，曲线下面积(AUC)=69.6%,95%置信区间(CI)，0.661-0.732;试验装置,AUC=68.4%，95%CI，0.609-0.752]和性能最高的影像组学模型(训练集，AUC=84.3%，95%CI，0.812-0.873；测试集，AUC=72.4%，95%CI，0.653-0.794)模型，CNN模型(训练集，AUC=94.3%，95%CI，0.920-0.961；测试集，AUC=94.7%，95%CI，0.894-0.978)对预测EGFR突变状态具有显著更好的预测性能。此外,与综合模型(训练集，AUC=95.7%，95%CI，0.942-0.971；测试集，AUC=87.4%，95%CI，0.820-0.924），CNN模型具有较好的稳定性。
■CNN模型在非侵入性预测肺腺癌患者的EGFR突变状态方面具有出色的性能，有望成为临床医生的辅助工具。
