Nomogram Development for Predicting Synchronous Lung Metastasis in Patients with T1 Colorectal Cancer: An SEER-Based Analysis.
: Colorectal cancer is a significant global health burden, with lung metastasis contributing substantially to mortality.
- 95% CI 0.710-0.746
APA
Chen PC, Kao YK, et al. (2026). Nomogram Development for Predicting Synchronous Lung Metastasis in Patients with T1 Colorectal Cancer: An SEER-Based Analysis.. Medicina (Kaunas, Lithuania), 62(3). https://doi.org/10.3390/medicina62030431
MLA
Chen PC, et al.. "Nomogram Development for Predicting Synchronous Lung Metastasis in Patients with T1 Colorectal Cancer: An SEER-Based Analysis.." Medicina (Kaunas, Lithuania), vol. 62, no. 3, 2026.
PMID
41901515
Abstract
: Colorectal cancer is a significant global health burden, with lung metastasis contributing substantially to mortality. Accurate risk stratification of synchronous lung metastasis (sLM) in patients with T1 colorectal cancer is important for informing staging decisions, yet no validated tool exists to guide selective chest computed tomography (CT) in this population. This study aimed to develop and validate two complementary nomograms: a clinicopathologic model (Model A) for pre-imaging risk stratification to guide chest CT decisions, and a post-staging model (Model B) incorporating concurrent organ metastasis status for comprehensive risk profiling. : We utilized data from the Surveillance, Epidemiology, and End Results database, including patients diagnosed with T1 colorectal cancer between 2010 and 2020. Logistic regression analyses identified significant predictors of synchronous lung metastasis. A nomogram was constructed based on these predictors and validated using a split-sample approach. : The study included 41,728 patients with T1 colorectal cancer. Significant predictors of synchronous lung metastasis included tumor grade, size, location, lymph node involvement, and concurrent metastases in other organs. Two models were developed: Model A (clinicopathologic-only) demonstrated moderate discriminatory ability (AUC = 0.728, 95% CI: 0.710-0.746), while Model B (including concurrent organ metastasis status) demonstrated good discrimination (AUC = 0.856, 95% CI: 0.843-0.869): Model A validation AUC = 0.716; Model B validation AUC = 0.849. Calibration plots showed good agreement between predicted and observed probabilities of synchronous lung metastasis. : This study developed and internally validated two nomograms for predicting sLM in patients with T1 CRC. Model A, using readily available clinicopathological factors, may support selective chest CT decisions during initial staging. Model B, incorporating post-staging information, may assist in prognostic counseling. External validation is required before clinical implementation.
MeSH Terms
Humans; Nomograms; Lung Neoplasms; Colorectal Neoplasms; Female; Male; Middle Aged; SEER Program; Aged; Tomography, X-Ray Computed; Neoplasm Staging; Risk Assessment; Adult; Logistic Models
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