ArticleBMC surgery2023
Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients.
Article in BMC surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A predictive model for the risk of death within 30 days in patients with cardiorenal syndrome treated with continuous renal replacement therapy: A retrospective study based on the MIMIC-IV database.Scientific reports · 2026Article
- Development of multi-algorithm machine learning models integrating novel serum biomarkers for survival prediction in colorectal cancer: a retrospective cohort study.Frontiers in oncology · 2026Article
- A dynamic nomogram and risk stratification system for predicting cancer-specific survival in patients with locally advanced differentiated thyroid cancer: a population-based study.Gland surgery · 2025Article
- Nomogram model for predicting the long-term prognosis of cervical cancer patients: a population-based study in Mato Grosso, Brazil.BMC cancer · 2025Article
- Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III-IV Colorectal Cancer Patients.Cancer medicine · 2024Article
- Evolution of Colorectal Cancer Trends and Treatment Outcomes: A Comprehensive Retrospective Analysis (2019-2023) in West Kazakhstan.Asian Pacific journal of cancer prevention : APJCP · 2024Article
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8 authors.
Funding
Abstract
backgroundColorectal cancer (CRC) is a frequent cancer worldwide with varied survival outcomes.
objectiveWe aimed to develop a nomogram model to predict the overall survival (OS) of CRC patients after surgery.
designThis is a retrospective study.
settingThis study was conducted from 2015 to 2016 in a single tertiary center for CRC. PATIENTS: CRC patients who underwent surgery between 2015 and 2016 were enrolled and randomly assigned into the training (n = 480) and validation (n = 206) groups. The risk score of each subject was calculated based on the nomogram. All participants were categorized into two subgroups according to the median value of the score.
main outcome measuresThe clinical characteristics of all patients were collected, significant prognostic variables were determined by univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. The tuning parameter (λ) for LASSO regression was determined by cross-validation. Independent prognostic variables determined by multivariable analysis were used to establish the nomogram. The predictive capacity of the model was assessed by risk group stratification.
resultsInfiltration depth, macroscopic classification, BRAF, carbohydrate antigen 19 - 9 (CA-199) levels, N stage, M stage, TNM stage, carcinoembryonic antigen levels, number of positive lymph nodes, vascular tumor thrombus, and lymph node metastasis were independent prognostic factors. The nomogram established based on these factors exhibited good discriminatory capacity. The concordance indices for the training and validation groups were 0.796 and 0.786, respectively. The calibration curve suggested favorable agreement between predictions and observations. Moreover, the OS of different risk subgroups was significantly different. LIMITATIONS: The limitations of this work included small sample size and single-center design. Also, some prognostic factors could not be included due to the retrospective design.
conclusionsA prognostic nomogram for predicting the OS of CRC patients after surgery was developed, which might be helpful for evaluating the prognosis of CRC patients.
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