Related Experiment Video
Updated: Aug 26, 2026
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Improved prediction of recurrence after curative resection of colon carcinoma using tree-based risk stratification
Martin Radespiel-Tröger1, Werner Hohenberger, Bertram Reingruber
1Department of Medical Informatics, Biometry, and Epidemiology, University of Erlangen-Nuernberg, Erlangen, Germany. radespmn@imbe.imed.uni-erlangen.de
Background:
Patients who are at high risk of recurrence after undergoing curative (R0) resection for colon carcinoma may benefit most from adjuvant treatment and from intensive follow-up for early detection and treatment of recurrence. However, in light of new clinical evidence, there is a need for continuous improvement in the calculation of the risk of recurrence.
Methods:
Six hundred forty-one patients with R0-resected colon carcinoma who underwent surgery between January 1, 1984 and December 31, 1996 were recruited from the Erlangen Registry of Colorectal Carcinoma. The study end point was time until first locoregional or distant recurrence. The factors analyzed were: age, gender, site in colon, International Union Against Cancer (UICC) pathologic tumor classification (pT), UICC pathologic lymph node classification, histologic tumor type, malignancy grade, lymphatic invasion, venous invasion, number of examined lymph nodes, number of lymph node metastases, emergency presentation, intraoperative tumor cell spillage, surgeon, and time period. The resulting prognostic tree was evaluated by means of an independent sample using a measure of predictive accuracy based on the Brier score for censored data. Predictive accuracy was compared with several proposed stage groupings.
Results:
The prognostic tree contained the following variables: pT, the number of lymph node metastases, venous invasion, and emergency presentation. Predictive accuracy based on the validation sample was 0.230 (95% confidence interval [95% CI], 0.227-0.233) for the prognostic tree and 0.212 (95% CI, 0.209-0.215) for the UICC TNM sixth edition stage grouping.
Conclusions:
The prognostic tree showed superior predictive accuracy when it was validated using an independent sample. It is interpreted easily and may be applied under clinical circumstances. Provided that their classification system can be validated successfully in other centers, the authors propose using the prognostic tree as a starting point for studies of adjuvant treatment and follow-up strategies.
