A Clinical Nomogram Predicting Pathologic Lymph Node Involvement in Esophageal Cancer Patients

In Brief Background: Esophageal cancer patients with pathologic lymph-node involvement (pN1) generally have a poor prognosis with surgery alone. We, therefore, constructed a nomogram to predict the risk of pN1 prior to surgical resection and externally validated the clinical utility of the model. Methods: A total of 273 esophageal adenocarcinoma patients treated with surgery alone were reviewed from 2 different institutions (University of Texas M. D. Anderson Cancer Center = 164, training set; University of Rochester School of Medicine and Dentistry = 109, validation set). Pretreatment clinical parameters were used to construct a nomogram for predicting the risk of pN1. Internal and external validation of the nomogram was performed to assess clinical utility. Results: Of the 164 patients in the training set, 56 patients (34%) had lymph-node involvement (pN1). Significant factors associated with pN1 on univariable logistic regression analysis (using a P 2 cm (odds ratio, 7.0; 95% confidence interval, 2.7–18.1; P < 0.001). Regression tree analysis was used to determine the best cutoff for cL. A nomogram was created for pN1 using these clinical parameters and was internally validated by bootstrapping with a predicted accuracy of 85.1%. External validation performed on the validation set demonstrated an original C-index of 0.777 suggesting good clinical utility. Conclusions: Our analyses demonstrate that the risk of pathologic nodal involvement in esophageal adenocarcinoma patients can be estimated by this clinical nomogram, which will allow the identification of patients at high-risk of harboring positive lymph-nodes, who may be candidates for en bloc resection and/or neoadjuvant treatment. Pathological lymph node involvement portends a poor prognosis for patients with esophageal cancer. However, it is difficult to identify these patients with current imaging methods. The purpose of this study was to identify reliable clinical factors that can be used to identify patients with N1 disease, who may eventually benefit from neoadjuvant therapy.