Lymphovascular invasion (LVI) is mostly used as a preoperative predictor to establish lymph node metastasis (LNM) prediction models for superficial esophageal squamous cell carcinoma (SESCC). However, LVI still needs to be confirmed by postoperative pathology. In this study, we combined LNM and LVI as a unified outcome and named it LNM/LVI, and aimed to develop an LNM/LVI prediction model in SESCC using preoperative factors.
A total of 512 patients who underwent radical resection of SESCC were retrospectively collected. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were adopted to identify the predictive factors of LNM/LVI. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. A nomogram for predicting LNM/LVI was established by incorporating these factors. The efficacy, accuracy, and clinical utility of the nomogram were, respectively, assessed with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Finally, the random forest (RF) algorithm was used to further evaluate the impact of these factors included in the nomogram on LNM/LVI.
Tumor size, tumor location, tumor invasion depth, tumor differentiation, and macroscopic type were confirmed as independent risk factors for LNM/LVI according to the results of logistic regression, LASSO regression, IDI, and NRI analyses. A nomogram including these five variables showed a good performance in LNM/LVI prediction (AUC = 0.776). The calibration curve revealed that the predictive results of this nomogram were nearly consistent with actual observations. Significant clinical utility of our nomogram was demonstrated by DCA. The RF model with the same five variables also had similar predictive efficacy with the nomogram (AUC = 0.775).
The nomogram was adopted as a final tool for predicting LNM/LVI because its risk score system made it more user-friendly and clinically useful than the random forest model, which can help clinicians make optimal treatment decisions for patients with SESCC.