Nomogram including tumor deposition count to noninvasively evaluate the prognosis of rectal cancer patients: A retrospective study

Abstract

To build a nomogram model that includes tumor deposition (TDs) count to noninvasively evaluate the prognosis of patients with rectal cancer (RC). A total of 262 patients between January 2013 and December 2018 were recruited and divided into 2 cohorts: training (n = 171) and validation (n = 91). Axial portal venous phase computed tomography images were used to extract radiomic features, and the least absolute shrinkage and selection operator-Cox analysis was applied to develop an optimal radiomics model to derive the Rad-score. A Cox regression model combining clinicopathological factors and Rad-scores was constructed and visualized using a nomogram. And its ability to predict RC patients’ survival was tested by Kaplan–Meier survival analysis. The time-dependent concordance index curve was used to demonstrate the differentiation degree of model. Calibration and decision curve analyses were used to evaluate the calibration accuracy and clinical usefulness of the nomogram model, and the prediction performance of the nomogram model was compared with the clinical and radiomics models using the likelihood test. Computed tomography-based Rad-score, pathological tumor (pT) stageT4, and TDs count were independent risk factors affecting the prognosis of RC. The whole concordance index of the nomogram model for predicting the overall survival rates of RC was higher than that of the clinical and radiomics models in the training (0.812 vs 0.59, P = .019; 0.812 vs 0.714, P = .014) and validation groups (0.725 vs 0.585, P = .002; 0.725 vs 0.751, P = .256). The nomogram model could effectively predict patients’ overall survival rate (hazard ratio = 9.25, 95% CI = [1.17–72.99], P = .01). The nomogram model also showed a higher clinical net benefit than the clinical and radiomics models in the training and validation groups. The nomogram model developed in this study can be used to noninvasively evaluate the prognosis of RC patients. The TDs count is an independent risk factor for the prognosis of RC.