Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
BMC Cancer. 2021 Jan 5;21(1):22. doi: 10.1186/s12885-020-07738-9.
An accurate intraoperative prediction of lymph node metastatic risk can help surgeons in choosing precise surgical procedures. We aimed to develop and validate nomograms to intraoperatively predict patterns of regional lymph node (LN) metastasis in patients with esophageal cancer.
The prediction model was developed in a training cohort consisting of 487 patients diagnosed with esophageal cancer who underwent esophagectomy with complete LN dissection from January 2016 to December 2016. Univariate and multivariable logistic regression were used to identify independent risk factors that were incorporated into a prediction model and used to construct a nomogram. Contrast-enhanced computed tomography reported LN status and was an important comparative factor of clinical usefulness in a validation cohort. Nomogram performance was assessed in terms of calibration, discrimination, and clinical usefulness. An independent validation cohort comprised 206 consecutive patients from January 2017 to December 2017.
Univariate analysis and multivariable logistic regression revealed three independent predictors of metastatic regional LNs, three independent predictors of continuous regional LNs, and two independent predictors of skipping regional LNs. Independent predictors were used to build three individualized prediction nomograms. The models showed good calibration and discrimination, with area under the curve (AUC) values of 0.737, 0.738, and 0.707. Application of the nomogram in the validation cohort yielded good calibration and discrimination, with AUC values of 0.728, 0.668, and 0.657. Decision curve analysis demonstrated that the three nomograms were clinically useful in the validation cohort.
This study presents three nomograms that incorporate clinicopathologic factors, which can be used to facilitate the intraoperative prediction of metastatic regional LN patterns in patients with esophageal cancer.
准确预测淋巴结转移风险有助于外科医生选择精确的手术方式。本研究旨在开发和验证列线图模型，以预测食管癌患者术中区域淋巴结（LN）转移模式。
在包含 487 例食管癌患者的训练队列中，我们建立了预测模型，这些患者于 2016 年 1 月至 2016 年 12 月期间接受了食管癌根治术及完全的淋巴结清扫术。单因素和多因素逻辑回归分析确定了纳入预测模型的独立危险因素，并构建了列线图。增强 CT 报告的淋巴结状态是验证队列中临床实用性的重要比较因素。通过校准、判别和临床实用性评估列线图性能。来自 2017 年 1 月至 2017 年 12 月的 206 例连续患者组成独立验证队列。
单因素分析和多因素逻辑回归分析显示，转移性区域 LN 的三个独立预测因素、连续区域 LN 的三个独立预测因素和跳跃性区域 LN 的两个独立预测因素。独立预测因素被用于构建三个个体化预测列线图。模型显示出良好的校准和判别能力，曲线下面积（AUC）值分别为 0.737、0.738 和 0.707。验证队列中列线图的应用也显示出良好的校准和判别能力，AUC 值分别为 0.728、0.668 和 0.657。决策曲线分析表明，三个列线图在验证队列中具有临床实用性。
本研究提出了三个列线图模型，包含临床病理因素，有助于术中预测食管癌患者的转移性区域 LN 模式。
