Nomogram for Predicting Early Death in Patients with Metastatic Colon Cancer Based on SEER Database
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摘要:目的
构建预测转移性结肠癌（mCC）患者早期死亡的列线图模型。
方法从SEER数据库中选择6 669例符合条件的mCC患者。根据多因素Logistic回归中的危险因素构建列线图。通过C-index、校准曲线和临床决策曲线分析（DCA）评估列线图的预测性能。
结果原发肿瘤位置、肿瘤分化、T分期、M分期、骨转移、脑转移、CEA、肿瘤大小、年龄和婚姻状态是mCC患者早期死亡的独立影响因素。基于这些变量构建列线图，C-index和校准曲线显示模型具有很好的预测能力，DCA曲线显示列线图可以使患者有较好的临床获益。
结论该列线图具有良好的预测能力，能够帮助医生识别可能早期死亡的高危mCC患者，有助于制定个性化治疗策略。
Abstract:ObjectiveTo construct a Nomogram model that can accurately predict early death of metastatic colon cancer (mCC).
MethodsA total of 6 669 patients from the SEER database were identified using inclusion and exclusion criteria. Multivariate logistic regression was used to identify risk factors for early mortality and to construct a Nomogram. The predictive performance of the Nomogram was evaluated by C-index, calibration curve, and decision curve analysis (DCA).
ResultsPrimary tumor location, differentiation grade, T stage, M stage, bone metastases, brain metastases, CEA, tumor size, age and marital status were independent factors for early death in patients with mCC. A Nomogram was constructed based on these variables. The C-index and the calibration curve of the Nomogram showed the good predictive ability of the nomogram. DCA showed that the Nomogram had a superior clinical net benefit in predicting early death compared with TNM stage.
ConclusionThe developed Nomogram has good predictive ability and can help guide clinicians to identify patients with high-risk mCC for individualized diagnosis and treatment.
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Key words:
- Metastasis colon cancer /
- SEER /
- Nomogram /
- Early death
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Competing interests: The authors declare that they have no competing interests.作者贡献：王磊：资料收集、文献查阅、数据分析及论文撰写韩晖琼：文献查阅、资料收集审阅秦艳茹：研究方案设计、审阅
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图 2 预测mCC患者早期死亡风险的模型
Figure 2 Models to predicting the risk for early death in patients with metastatic colon cancer
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图 3 训练集(A)和验证集(B)列线图准确性的校准曲线
Figure 3 Calibration curves for evaluating prediction accuracy of nomogram on training(A) and validation(B) sets
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图 4 训练集(A)和验证集(B)列线图临床净效益的决策曲线分析曲线
Figure 4 Decision curve analysis for evaluating clinical net benefits of nomograms on training(A) and validation(B) sets
表 1 mCC患者的临床病理学特征(n(%))
Table 1 Clinicopathological characteristics of mCC patients (n(%))
下载: 导出CSV
表 2 训练队列中早期死亡风险因素的单变量和多变量Logistic回归分析
Table 2 Univariate and Multivariate Logistic Regression for Analyzing the Risk Factors for Early Death in the Training Cohort
下载: 导出CSV
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