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Predicting Lymph Node Metastasis Using Computed Tomography Radiomics Analysis in Patients With Resectable Esophageal Squamous Cell Carcinoma.

在可切除食管鳞状细胞癌患者中使用计算机断层扫描放射组学分析预测淋巴结转移。

  • 影响因子:1.48
  • DOI:10.1097/RCT.0000000000001125
  • 作者列表:"Zhao B","Zhu HT","Li XT","Shi YJ","Cao K","Sun YS
  • 发表时间:2021-03-01
Abstract

OBJECTIVES:We investigated the value of radiomics data, extracted from pretreatment computed tomography images of the primary tumor (PT) and lymph node (LN) for predicting LN metastasis in esophageal squamous cell carcinoma (ESCC) patients. MATERIALS AND METHODS:A total 338 ESCC patients were retrospectively assessed. Primary tumor, the largest short-axis diameter LN (LSLN), and PT and LSLN interaction term (IT) radiomic features were calculated. Subsequently, the radiomic signature was combined with clinical risk factors in multivariable logistic regression analysis to build various clinical-radiomic models. Model performance was evaluated with respect to the fit, overall performance, differentiation, and calibration. RESULTS:A clinical-radiomic model, which combined clinical and PT-LSLN-IT radiomic signature, showed favorable discrimination and calibration. The area under curve value was 0.865 and 0.841 in training and test set. CONCLUSIONS:A venous computed tomography radiomic model based on the PT, LSLN, and IT radiomic features represents a novel noninvasive tool for prediction LN metastasis in ESCC.

摘要

目的: 我们研究了从原发性肿瘤 (PT) 和淋巴结 (LN) 的预处理计算机断层扫描图像中提取的放射组学数据预测食管鳞状细胞癌 (ESCC) 患者LN转移的价值。 材料和方法: 回顾性评估338例ESCC患者。计算原发肿瘤、最大短轴直径LN (LSLN) 以及PT和LSLN相互作用项 (IT) 放射组学特征。随后,在多变量逻辑回归分析中将放射组学特征与临床风险因素相结合,以构建各种临床-放射组学模型。关于拟合、总体性能、差异和校准评估模型性能。 结果: 结合临床和PT-LSLN-IT放射组学特征的临床-放射组学模型显示出有利的区分和校准。在训练和测试集中,曲线下面积值分别为0.865和0.841。 结论: 基于PT、LSLN和IT放射组学特征的静脉计算机断层扫描放射组学模型代表了一种预测ESCC中LN转移的新型无创工具。

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