Introduction: The limitations of preoperative examination result in locally advanced esophageal squamous cell carcinoma (ESCC) often going undetected preoperatively. This study aimed to develop a clinical tool for identifying patients at high risk for occult locally advanced ESCC; the tool can be supplemented with preoperative examination to improve the reliability of preoperative staging.
Materials and methods: Data of 598 patients who underwent radical resection of ESCC from 2010 to 2017 were analyzed. Logistic multivariate analysis was used to develop a nomogram. The training cohort included patients who underwent surgery during an earlier period (n = 426), and the validation cohort included those who underwent surgery thereafter (n = 172), to confirm the model's performance. Nomogram discrimination and calibration were evaluated using Harrell's concordance index (C-index) and calibration plots, respectively.
Results: Logistic multivariate analysis suggested that higher preoperative carcinoembryonic antigen levels (>2.43, odds ratio [OR]: 2.093; 95% confidence interval [CI], 1.233-2.554; P = 0.006), presence of preoperative symptoms (OR: 2.737; 95% CI, 1.194-6.277; P = 0.017), presence of lymph node enlargement (OR: 2.100; 95% CI, 1.243-3.550; P = 0.006), and advanced gross aspect (OR: 13.103; 95% CI, 7.689-23.330; P < 0.001) were independent predictors of occult locally advanced ESCC. Based on these predictive factors, a nomogram was developed. The C-indices of the training and validation cohorts were 0.827 and 0.897, respectively, indicating that the model had a good predictive performance. To evaluate the accuracy of the model, we divided patients into high-risk and low-risk groups according to their nomogram scores, and a comparison was made with histopathological data.
Conclusion: The nomogram achieved a good preoperative prediction of occult locally advanced ESCC; it can be used to make rational therapeutic choices.
Keywords: esophageal squamous cell carcinoma; neoadjuvant therapy; nomogram; occult lymph node metastasis; predictor factors.
Copyright © 2022 Huang, Hong and Kang.