Predicting lymph node metastasis in colorectal cancer patients: development and validation of a column chart model

Abstract

Lymph node metastasis (LNM) is one of the crucial factors in determining the optimal treatment approach for colorectal cancer. The objective of this study was to establish and validate a column chart for predicting LNM in colon cancer patients. We extracted a total of 83,430 cases of colon cancer from the Surveillance, Epidemiology, and End Results (SEER) database, spanning the years 2010-2017. These cases were divided into a training group and a testing group in a 7:3 ratio. An additional 8545 patients from the years 2018-2019 were used for external validation. Univariate and multivariate logistic regression models were employed in the training set to identify predictive factors. Models were developed using logistic regression, LASSO regression, ridge regression, and elastic net regression algorithms. Model performance was quantified by calculating the area under the ROC curve (AUC) and its corresponding 95% confidence interval. The results demonstrated that tumor location, grade, age, tumor size, T stage, race, and CEA were independent predictors of LNM in CRC patients. The logistic regression model yielded an AUC of 0.708 (0.7038-0.7122), outperforming ridge regression and achieving similar AUC values as LASSO regression and elastic net regression. Based on the logistic regression algorithm, we constructed a column chart for predicting LNM in CRC patients. Further subgroup analysis based on gender, age, and grade indicated that the logistic prediction model exhibited good adaptability across all subgroups. Our column chart displayed excellent predictive capability and serves as a useful tool for clinicians in predicting LNM in colorectal cancer patients.

Keywords: Colorectal cancer; Lymphatic metastasis; Nomogram; SEER; Treatment programs.

Fig. 1

Nomogram for predicting lymph node metastasis (LNM) in colorectal cancer (CRC) patients

Fig. 2

Receiver operator characteristic (ROC) curves and the area under the ROC curve (AUC) for the logistic regression prediction model in the training set, test set, and external validation. A ROC curves in the training set; B ROC curves in the test set; C ROC curves in the external validation

Fig. 3

Calibration plots.: Show the consistency of the predicted potentiality and actual values。A–C The consistency of the predicted potentiality and actual values in the training set、the test set and in the external validation. D, E Decision curve analysis (DCA). Assessing clinical utility in the training set、the test set and in the external validation