Original Article
Preoperative assessment of lymph node metastasis in clinically node-negative rectal cancer patients based on a nomogram consisting of five clinical factors
Abstract
Background: Currently, reliable approaches for accurate assessment of lymph node metastases (LNM), which is an important indication of preoperative chemoradiotherapy (CRT), are not available for clinically node-negative rectal cancer patients. This study aims to identify clinical factors associated with LNM and to establish a nomogram for LNM prediction in clinically node-negative rectal cancer patients.
Methods: The least absolute shrinkage and selection operator (LASSO) aggression and multivariate logistic regression analyses were applied to identify clinical factors associated with LNM. A nomogram was established to predict the probability of LNM in clinically node-negative rectal cancer patients based on the multivariate logistic regression model.
Results: Six potential risk factors were selected on the basis of LASSO aggression analysis, and five of them were identified as independent risk factors for LNM based on multivariate analysis, including MRI-reported tumor location, clinical T classification, MRI-reported tumor diameter, white blood cell count (WBC), and preoperative elevated tumor markers. A nomogram consisting of the five clinical factors was established and showed good discrimination. Decision curve analysis demonstrated that the established nomogram was reliable and accurate for LNM prediction in clinically node-negative rectal cancer patients.
Conclusions: A nomogram based on five clinical factors, including MRI-reported tumor location, clinical T classification, MRI-reported tumor diameter, WBC, and preoperative elevated tumor markers, are useful for assessing LNM in clinically node-negative rectal cancer patients, which is important for preoperative CRT regimens.
Methods: The least absolute shrinkage and selection operator (LASSO) aggression and multivariate logistic regression analyses were applied to identify clinical factors associated with LNM. A nomogram was established to predict the probability of LNM in clinically node-negative rectal cancer patients based on the multivariate logistic regression model.
Results: Six potential risk factors were selected on the basis of LASSO aggression analysis, and five of them were identified as independent risk factors for LNM based on multivariate analysis, including MRI-reported tumor location, clinical T classification, MRI-reported tumor diameter, white blood cell count (WBC), and preoperative elevated tumor markers. A nomogram consisting of the five clinical factors was established and showed good discrimination. Decision curve analysis demonstrated that the established nomogram was reliable and accurate for LNM prediction in clinically node-negative rectal cancer patients.
Conclusions: A nomogram based on five clinical factors, including MRI-reported tumor location, clinical T classification, MRI-reported tumor diameter, WBC, and preoperative elevated tumor markers, are useful for assessing LNM in clinically node-negative rectal cancer patients, which is important for preoperative CRT regimens.
