Related Experiment Video
Updated: Aug 19, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A clinical nomogram for predicting long-term survival in advanced colorectal cancer
C Massacesi1, A Norman, T Price
1The Department of Medicine and GI Unit, The Royal Marsden NHST, London and Sutton, SM2 5PT, Surrey, UK.
Abstract:
From our prospectively accrued database of patients with gastrointestinal cancer, 1057 patients with advanced colorectal cancer were identified with the aim of determining predictive factors for survival of greater than 2 years and to use this information to develop a predictive nomogram. Patient's baseline characteristics, type and number of chemotherapy regimens received, and response to chemotherapy were assessed by univariate and multivariate logistic regression comparing those who survived greater than or less than 2 years. A total of 161 (15.2%) patients survived more than 2 years, so-called long survivors (LS). In multivariate analysis, positive predictive factors for LS were: good performance status (PS), normal serum carcinoembryonic antigen (CEA), rectal primary, Dukes' stage A-B, well or moderate differentiation, two or less disease sites, response to chemotherapy and treatment used protracted venous infusion (PVI) 5-fluorouracil (5-FU) in first-line chemotherapy, and the increasing number of chemotherapy treatments received. From these PS, CEA, number of sites and response to first-line chemotherapy were used to develop a nomogram capable of predicting the probability of survival beyond 2 years for an individual patient. This large study confirmed the relevance of known prognostic factors in metastatic colorectal cancer and demonstrated the importance of response to chemotherapy as an independent factor to predict LS. By combining these, we developed a nomogram which provides information which is likely to prove useful in the management of patients with advanced colorectal cancer.
More Related Videos
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025