A model of multiple tumor marker for lymph node metastasis assessment in colorectal cancer: a retrospective study

Abstract

Background Assessment of colorectal cancer (CRC) lymph node metastasis (LNM) is critical to the decision of surgery, prognosis, and therapy strategy. In this study, we aimed to develop and validate a multiple tumor marker nomogram for predicting LNM in CRC patients. Methods A total of 674 patients who met the inclusion criteria were collected and randomly divided into primary cohort and internal test cohort at a ratio of 7:3. An external test cohort enrolled 178 CRC patients from the West China Hospital. Clinicopathologic variables were obtained from electronic medical records. The least absolute shrinkage and selection operator (LASSO) and interquartile range analysis were carried out for variable dimensionality reduction and feature selection. Multivariate logistic regression analysis was conducted to develop predictive models of LNM. The performance of the established models was evaluated by the receiver operating characteristic (ROC) curve, calibration belt, and clinical usefulness. Results Based on minimum criteria, 18 potential features were reduced to six predictors by LASSO and interquartile range in the primary cohort. The model demonstrated good discrimination and ROC curve (AUC = 0.721 in the internal test cohort, AUC = 0.758 in the external test cohort) in LNM assessment. Good calibration was shown for the probability of CRC LNM in the internal and external test cohorts. Decision curve analysis illustrated that multi-tumor markers nomogram was clinically useful. Conclusions The study proposed a reliable nomogram that could be efficiently and conveniently utilized to facilitate the assessment of individually-tailored LNM in patients with CRC, complementing imaging and biopsy tests.

Development of a blood proteins-based model for bronchopulmonary dysplasia prediction in premature infants

“…In the present study, we selected 8 predictors for BPD prediction through LASSO regression, a method which has been widely used in feature selection and model development [ 29 , 30 ]. Our model could achieve an of AUC (95% CI) 1.00 (0.99-1.00) and 0.96 (0.90-1.00) in training cohort and test cohort, respectively.…”

Comprehensive analyses of m6A RNA methylation patterns and related immune microenvironment in idiopathic pulmonary arterial hypertension

“…Calibration curves were constructed to compare the heterogenicity between the observed and the predicted values. Then, we used decision curve analysis (DCA) ( Fu J. et al, 2022 ) and a clinical impact curve to validate the clinical effectiveness of nomograms.…”