Related Experiment Video
Updated: Jun 12, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Survival prediction in sigmoid colon cancer patients with liver metastasis: a prospective cohort study
Shuai Shao1, Dan Tian2,3, Mingyang Li1
1Department of Immunology, Beijing Clinical Research Institute, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background:
Sigmoid colon cancer is a common type of colorectal cancer, frequently leading to liver metastasis. Predicting cause-specific survival and overall survival in patients with sigmoid colon cancer metastasis to liver is challenging because of the lack of suitable models.
Methods:
Patients with sigmoid colon cancer metastasis to liver (2010-2017) in the Surveillance, Epidemiology, and End Results (SEER) Program were recruited. Patients were split into training and validation groups (7:3). Prognostic factors were identified using competing risk and Cox proportional hazards models, and nomograms for cause-specific survival and overall survival were developed. Model performance was evaluated with the concordance index and calibration curves, with a 2-sided P value less than .05 considered statistically significant.
Results:
A total of 4981 sigmoid colon cancer with liver metastasis patients were included, with a median follow-up of 20 months (interquartile range [IQR] = 9-33 months). During follow-up, 72.25% of patients died (68.44% from sigmoid colon cancer, 3.81% from other causes). Age, race, grade, T stage, N stage, surgery, chemotherapy, carcinoembryonic antigen, tumor deposits, lung metastasis, and tumor size were prognostic factors for cause-specific survival and overall survival. The models demonstrated good discrimination and calibration performance, with C index values of 0.79 (95% confidence interval [CI] = 0.78 to 0.80) for cause-specific survival and 0.74 (95% CI = 0.73 to 0.75) for overall survival. A web-based application for real-time cause-specific survival predictions was created, accessible at https://shuaishao.shinyapps.io/SCCLM/.
Conclusion:
Prognostic factors for sigmoid colon cancer with liver metastasis patients were identified based on the SEER database, and nomograms for cause-specific survival and overall survival showed good performance. A web-based application was developed to predict sigmoid colon cancer with liver metastasis-specific survival, aiding in survival risk stratification.
