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Updated: Aug 12, 2026
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Lipid profile-based prognostic nomograms for obstructive colorectal cancer: multi-cohort development and validation
Hanwenchen Wang1, Falong Zou1, Denglong Cheng1
1Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Frontiers in Medicine
|August 11, 2026
Summary
New nomograms predict survival for obstructive colorectal cancer (OCRC) patients. These tools integrate lipid and clinicopathological data, offering improved individualized risk assessment beyond traditional staging for better patient outcomes.
Area of Science:
- Oncology
- Biostatistics
Background:
- Obstructive colorectal cancer (OCRC) presents a poor prognosis.
- Current prognostic tools like TNM staging lack sufficient accuracy for OCRC patients.
Purpose of the Study:
- To develop and validate nomograms for predicting overall survival (OS) and disease-free survival (DFS) in OCRC patients.
- To improve individualized risk prediction for OCRC.
Main Methods:
- Retrospective analysis of 1,650 OCRC patients from two centers.
- Identification of independent prognostic factors using Cox regression.
- Construction and validation of OS and DFS nomograms using ROC curves, C-index, calibration curves, and decision curve analysis (DCA).
Main Results:
- The OS nomogram, including 8 factors, demonstrated strong predictive performance across training, internal, and external validation cohorts (AUCs 0.723-0.856).
- The DFS nomogram, with 6 factors, also showed excellent predictive accuracy (AUCs 0.750-0.841).
- All evaluations confirmed the nomograms' excellent discrimination, calibration, and clinical utility.
Conclusions:
- Validated nomograms incorporating lipid and clinicopathological factors effectively predict OS and DFS in OCRC patients.
- These nomograms offer enhanced tools for individualized risk stratification and clinical decision-making in OCRC management.
