Zhou Yue, Song Lijie, Xia Jin, Liu Huan, Xing Jingjing, Gao Jianbo
Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of Oncology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Oncol. 2023 Aug 18;13:1225180. doi: 10.3389/fonc.2023.1225180. eCollection 2023.
Limited studies have observed the prognostic value of CT images for esophageal neuroendocrine carcinoma (NEC) due to rare incidence and low treatment experience in clinical. In this study, the pretreatment enhanced CT texture features and clinical characteristics were investigated to predict the overall survival of esophageal NEC.
This retrospective study included 89 patients with esophageal NEC. The training and testing cohorts comprised 61 (70%) and 28 (30%) patients, respectively. A total of 402 radiomics features were extracted from the tumor region that segmented pretreatment venous phase CT images. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to feature dimension reduction, feature selection, and radiomics signature construction. A radiomics nomogram was constructed based on the radiomics signature and clinical risk factors using a multivariable Cox proportional regression. The performance of the nomogram for the pretreatment prediction of overall survival (OS) was evaluated for discrimination and calibration.
Only the enhancement degree was an independent factor in clinical variable influenced OS. The radiomics signatures demonstrated good predictability for prognostic status discrimination. The radiomics nomogram integrating texture signatures was slightly superior to the nomogram derived from the combined model with a C-index of 0.844 (95%CI: 0.783-0.905) and 0.847 (95% CI: 0.782-0.912) in the training set, and 0.805 (95%CI: 0.707-0.903) and 0.745 (95% CI: 0.639-0.851) in the testing set, respectively.
The radiomics nomogram based on pretreatment CT radiomics signature had better prognostic power and predictability of the overall survival in patients with esophageal NEC than the model using combined variables.
由于食管神经内分泌癌（NEC）发病率低且临床治疗经验有限，关于CT图像对其预后价值的研究较少。本研究旨在探讨食管NEC患者治疗前增强CT纹理特征和临床特征对总生存期的预测价值。
本回顾性研究纳入89例食管NEC患者。训练组和测试组分别包括61例（70%）和28例（30%）患者。从分割后的治疗前静脉期CT图像肿瘤区域提取了402个影像组学特征。采用最小绝对收缩和选择算子（LASSO）Cox回归进行特征降维、特征选择和影像组学特征构建。基于影像组学特征和临床危险因素，采用多变量Cox比例回归构建影像组学列线图。评估列线图对总生存期（OS）治疗前预测的辨别力和校准性能。
在影响OS的临床变量中，仅强化程度是独立因素。影像组学特征对预后状态辨别具有良好的预测能力。在训练集中，整合纹理特征的影像组学列线图略优于联合模型列线图，C指数分别为0.844（95%CI：0.783 - 0.905）和0.847（95%CI：0.782 - 0.912）；在测试集中，C指数分别为0.805（95%CI：0.707 - 0.903）和0.745（95%CI：0.639 - 0.851）。
基于治疗前CT影像组学特征的影像组学列线图在预测食管NEC患者总生存期方面，比使用联合变量的模型具有更好的预后能力和预测性。
