CT影像组学特征预测晚期食管鳞状细胞癌淋巴结转移及区分区域和非区域淋巴结转移：一项病例对照研究
CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study.
作者信息
Ou Jing, Wu Lan, Li Rui, Wu Chang-Qiang, Liu Jun, Chen Tian-Wu, Zhang Xiao-Ming, Tang Sun, Wu Yu-Ping, Yang Li-Qin, Tan Bang-Guo, Lu Fu-Lin
机构信息
Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
出版信息
Quant Imaging Med Surg. 2021 Feb;11(2):628-640. doi: 10.21037/qims-20-241.
BACKGROUND
Prediction of lymph node status in esophageal squamous cell carcinoma (ESCC) is critical for clinical decision making. In clinical practice, computed tomography (CT) has been frequently used to assist in the preoperative staging of ESCC. Texture analysis can provide more information to reflect potential biological heterogeneity based on CT. A nomogram for the preoperative diagnosis of lymph node metastasis in patients with resectable ESCC has been previously developed. However, to the best of our knowledge, no reports focus on developing CT radiomics features to discriminate ESCC patients with regional lymph node metastasis (RLNM) and non-regional lymph node metastasis (NRLNM). We, therefore, aimed to develop CT radiomics models to predict lymph node metastasis (LNM) in advanced ESCC and to discriminate ESCC between RLNM and NRLNM.
METHODS
This study enrolled 334 patients with pathologically confirmed advanced ESCC, including 152 patients without LNM and 182 patients with LNM, and 103 patients with RLNM and 79 patients NRLNM. Radiomics features were extracted from CT data for each patient. The least absolute shrinkage and selection operator (LASSO) model and independent samples t-tests or Mann-Whitney U tests were exploited for dimension reduction and selection of radiomics features. Optimal radiomics features were chosen using multivariable logistic regression analysis. The discriminating performance was assessed by area under the receiver operating characteristic curve (AUC) and accuracy.
RESULTS
The radiomics features were developed based on multivariable logistic regression and were significantly associated with LNM status in both the training and validation cohorts (P<0.001). The radiomics models could differentiate between patients with and without LNM (AUC =0.79 and 0.75, and accuracy =0.75 and 0.71 in the training and validation cohorts, respectively). In patients with LNM, the radiomics features could effectively differentiate between RLNM and NRLNM (AUC =0.98 and 0.95, and accuracy =0.94 and 0.83 in the training and validation cohorts, respectively).
CONCLUSIONS
CT radiomics features could help predict the LNM status of advanced ESCC patients and effectively discriminate ESCC between RLNM and NRLNM.
背景
食管癌鳞状细胞癌（ESCC）淋巴结状态的预测对于临床决策至关重要。在临床实践中，计算机断层扫描（CT）经常用于辅助ESCC的术前分期。纹理分析可以基于CT提供更多信息以反映潜在的生物学异质性。之前已经开发了一种用于可切除ESCC患者术前诊断淋巴结转移的列线图。然而，据我们所知，尚无报告聚焦于开发CT影像组学特征以区分有区域淋巴结转移（RLNM）和无区域淋巴结转移（NRLNM）的ESCC患者。因此，我们旨在开发CT影像组学模型以预测晚期ESCC中的淋巴结转移（LNM）并区分RLNM和NRLNM的ESCC。
方法
本研究纳入了334例经病理证实的晚期ESCC患者，包括152例无LNM患者和182例有LNM患者，以及103例RLNM患者和79例NRLNM患者。从每位患者的CT数据中提取影像组学特征。采用最小绝对收缩和选择算子（LASSO）模型以及独立样本t检验或曼-惠特尼U检验进行影像组学特征的降维和选择。使用多变量逻辑回归分析选择最佳影像组学特征。通过受试者操作特征曲线（AUC）下面积和准确性评估鉴别性能。
结果
基于多变量逻辑回归开发的影像组学特征在训练和验证队列中均与LNM状态显著相关（P<0.001）。影像组学模型能够区分有和无LNM的患者（训练和验证队列中的AUC分别为0.79和0.75，准确性分别为0.75和0.71）。在有LNM的患者中，影像组学特征能够有效区分RLNM和NRLNM（训练和验证队列中的AUC分别为0.98和0.95，准确性分别为0.94和0.83）。
结论
CT影像组学特征有助于预测晚期ESCC患者的LNM状态，并有效区分RLNM和NRLNM的ESCC。
Zotero 插件