Hou Kuei-Yuan, Chen Jyun-Ru, Wang Yung-Chen, Chiu Ming-Huang, Lin Sen-Ping, Mo Yuan-Heng, Peng Shih-Chieh, Lu Chia-Feng
Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
Department of Radiology, Cathay General Hospital, Taipei 106, Taiwan.
Cancers (Basel). 2022 Aug 4;14(15):3798. doi: 10.3390/cancers14153798.
Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model.
由于肿瘤异质性和治疗策略的不同，非小细胞肺癌（NSCLC）患者的预后存在差异。本研究旨在构建一种结合放射组学和临床特征的深度学习模型，以预测NSCLC患者的总生存期。为提高所提模型的可靠性，对增强计算机断层扫描（CECT）图像进行了符合图像生物标志物标准化倡议的放射组学分析以及整合多中心数据集的补偿方法。收集了来自两家医院的492例NSCLC患者的治疗前CECT图像和临床数据。采用以放射组学和临床特征为输入的深度神经网络架构DeepSurv。使用诊断后8、12和24个月的C指数和曲线下面积（AUC）评估生存预测模型的性能。结合八个放射组学特征和五个临床特征的生存预测性能优于仅基于放射组学或临床特征的预测。联合模型的C指数值在诊断后8、12和24个月分别达到0.74、0.75和0.75，AUC值分别为0.76、0.74和0.73。总之，结合治疗前CECT图像特征、病变特征和治疗策略，使用深度学习模型可以有效预测NSCLC患者的生存期。
