Factors influencing liver metastasis in young adult patients with colorectal cancer and construction of a nomogram model for predicting liver metastasis
10.3760/cma.j.cn115355-20240313-00115
- VernacularTitle:青年结直肠癌患者肝转移的影响因素及预测肝转移列线图模型的构建
- Author:
Weiyi MA
1
;
Lijun WANG
Author Information
1. 锦州医科大学盘锦市中心医院研究生培养基地，盘锦 124000
- Keywords:
Colorectal neoplasms;
Young adults;
Neoplasm metastasis;
Nomograms
- From:
Cancer Research and Clinic
2024;36(11):817-823
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the independent factors influencing liver metastasis in young adults with colorectal cancer and to construct a nomogram model for predicting the risk of liver metastasis.Methods:SEER*Stat software version 8.4.1 was used to screen the data of patients from 18 registry sites of the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. The data was updated in November 2020. A total of 1 023 patients under the age of 45 years pathologically diagnosed with young adult colorectal cancer were included, among which 325 cases had liver metastases. All patients were randomly divided into a training set (719 cases) and a validation set (304 cases) at a 7∶3 ratio. The proportions of patients with different age, gender, race, marital status, primary tumor site, tumor size, tumor differentiation degree, T stage, N stage, carcinoembryonic antigen (CEA) level, surgery and chemoradiotherapy treatments were compared between the 2 groups. Independent factors affecting the liver metastasis in young adult patients with colorectal cancer were identified by using univariate and multivariate logistic regression analyses, followed by the construction of a nomogram model for predicting the risk of liver metastasis. The predictive efficiency of a nomogram model was assessed by using the receiver operating characteristic (ROC) curve and the area under the curve (AUC); calibration curves and the Hosmer-Lemeshow goodness-of-fit test were used to assess the calibration, and decision curve analysis was used to evaluate the clinical utility and net benefit.Results:In the training set, 719 patients included 491 cases without liver metastasis and 228 cases with liver metastasis, while in the validation set, 304 patients included 207 cases without liver metastasis and 97 cases with liver metastasis. The differences in the proportions of patients with different age, gender, race, marital status, primary tumor site, tumor size, tumor differentiation degree, T stage, N stage, CEA level, surgery and chemoradiotherapy treatments between the training set and the validation set were not statistically significant (all P > 0.05). Multivariate logistic regression showed that male gender ( OR = 1.54, 95% CI: 1.02-2.32, P = 0.039), undifferentiation ( OR = 10.12, 95% CI: 2.07-49.46, P = 0.004), N staging (N 1 staging: OR = 5.96, 95% CI: 3.11-11.41, P < 0.001; N 2 staging: OR = 11.16, 95% CI: 5.66-22.02, P < 0.001), CEA positivity ( OR = 6.65, 95% CI: 4.38-10.10, P < 0.001), surgery of primary sites ( OR = 0.23, 95% CI: 0.10-0.52, P < 0.001), and radiotherapy ( OR = 0.56, 95% CI: 0.34-0.91, P = 0.019) were independent influencing factors of colorectal cancer young adult patients with liver metastasis. A nomogram model was constructed based on these factors. The AUC of the training set and validation set for predicting liver metastasis was 0.863 and 0.871, respectively. The Hosmer-Lemeshow goodness-of-fit test indicated a good model fit ( P = 0.862 for the training set, P = 0.623 for the validation set), and the calibration curve demonstrated a good consistency between the model for predicting the risk and the real risk, and the decision curve analysis showed a high clinical net benefit. Conclusions:The established nomogram model provides a reliable method for predicting the risk of liver metastasis in young adult patients with colorectal cancer.
