Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography.

Abstract

Risk evaluation of lymph node metastasis (LNM) in superficial colorectal cancer resected by endoscopic surgery is critical for determining subsequent therapeutic strategies, but the role of existing clinical methods, including computed tomography, remains limited. Features of the nomogram were determined by logistic regression analysis, and the performance was validated by calibration plots, ROC curves and DCA curves in both the training set and the validation set. A total of 608 consecutive superficial CRC cases were randomly divided into 426 training and 182 validation cases. Univariate and multivariate logistic regression analyses revealed that age < 50, tumour budding, lymphatic invasion and lower HDL levels were risk factors for LNM. Stepwise regression and the Hosmer‒Lemeshow goodness of fit test showed that the nomogram had good performance and discrimination, which was validated by ROC curves and calibration plots. Internal and external validation demonstrated that the nomogram had a higher C-index (training group, 0.749, validation group, 0.693). DCA and clinical impact curves graphically show that the use of the nomogram to predict LNM had remarkable predictive power. Finally, in comparison with CT diagnosis, the nomogram also visually showed higher superiority, as demonstrated by ROC, DCA and clinical impact curves. Using common clinicopathologic factors, a noninvasive nomogram for individualized prediction of LNM after endoscopic surgery was conveniently established. Nomograms have great superiority in the risk stratification of LNM compared with traditional CT imaging.