A Nomogram Model for Predicting Prognosis of Obstructive Colorectal Cancer

Abstract

Background: The prognosis of obstructive colorectal cancer (oCRC) is worse than non-obstructive CRC, but the individualized prediction model for the prognosis of oCRC patients has not been established. The aim of this study was to select prognostic predictors to built a Nomogram model to predic the prognosis of oCRC patients. Methods: A retrospective study was conducted on 181 oCRC partients between February 2012 to December 2017 from three medical hospitals. 129 patients in one of the hospitals were assigned to the training chort.Univariate and multivariate analysis were used to select independent prognostic indicators in a training cohort and a Nomogram model was constructed. 52 patients foom another two hospital were used as the testing cohort to validate the model.Results: Multivariate analysis illustrated the CEA [p=0.037, HR=2.872 (1.065-7.740)], N stage [N1 vs. N0, p=0.028, HR=3.187 (1.137-8.938) ; N2 vs. N0, p=0.010, HR=4.098 (1.393-12.051)] and surgical procedure [p=0.002, HR=0.299 (0.139-0.643)] were independent prognostic factors for OS of oCRC patients. These factors were used to construct the Nomogram model. Both internal and external validation shows it relatively accuracy.Conclusions: CEA, N stage and surgical procedure were independent prognostic factors for OS of oCRC patients, Which can be visually exhibited by Nomogram model.

Development and validation of nomogram for predicting lymph node metastasis in early gastric cancer

“…To date, there have been many studies globally on the characteristics and regularity of LNM in EGC, but there is still no accurate prediction model. An increasing number of scholars believe that nomograms are effective tools for predicting tumour progression and aiding clinical decision-making [5][6][7][8][9][10] Prediction models are widely used to predict diagnosis and evaluate prognosis [11,12] , tumour stage, recurrence, metastasis, patient survival, and therapeutic e cacy [13][14][15][16][17][18] , There have been numerous studies worldwide on nomograms for the prediction of LNM in EGC [19][20][21] . Cui [22] et al established a nomogram for predicting LNM in EGC, which included patient sex, tumour morphology, vascular invasion, lymphangiosarcoma thrombosis, tumour length, tumour invasion depth, and tumour differentiation degree.…”