Related Experiment Video
Updated: Oct 5, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Development and validation of a nomogram for predicting survival in patients with surgically resected lung invasive
Guochao Zhang1, Xuefei Wang2, Jia Jia3
1Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
Lung invasive mucinous adenocarcinoma (LIMA) is a unique and rare subtype of lung adenocarcinoma. We identified prognostic factors and developed a nomogram for predicting overall survival (OS) in LIMA patients after surgery.
Methods:
Patients diagnosed with LIMA between 2008 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were randomized into training (n=1,254) and test (n=538) cohorts. A nomogram was established using the prognostic signature from the training cohort after multivariable Cox regression analysis. We externally validated the nomogram in a group of 369 patients from China. We separately tested for accuracy and clinical practicability using Harrell's concordance-index (C-index), calibration plots, and decision curve analysis (DCA).
Results:
We included 2,161 patients in the analysis. Seven factors, all of which significantly affected OS, were incorporated into the final model, including age, sex, differentiation grade, the extent of surgery, lymphadenectomy, and T, N, and M stage. C-indexes for the training, test, and external validation cohorts were 0.735, 0.736, and 0.773, respectively. The areas under the time-dependent receiver operating characteristic curves at five years were 0.747, 0.798, and 0.777, respectively. The nomogram was discriminative and well-calibrated when applied to the test and external validation cohorts. Significant between-group differences in OS were observed when classifying groups by nomogram score (log-rank P<0.001). An online web server for clinical use was developed using the nomogram.
Conclusions:
The nomogram facilitates accurate prediction of survival for patients with LIMA and can be used to stratify clinical risk groups for individualized treatment.
