[1]陈奕殷,陈 鸿,吴聪莲.基于SEER数据库分析女性肺癌患者远处转移的危险因素[J].医学信息,2026,39(10):40-46.[doi:10.3969/j.issn.1006-1959.2026.10.006]
　CHEN Yiyin,CHEN Hong,WU Conglian.Analysis of Risk Factors for Distant Metastasis in Female Lung Cancer Patients Basedon the SEER Database[J].Journal of Medical Information,2026,39(10):40-46.[doi:10.3969/j.issn.1006-1959.2026.10.006]
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基于SEER数据库分析女性肺癌患者远处转移的危险因素()

医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
39卷
期数:
2026年10期
页码:
40-46
栏目:
临床信息学
出版日期:
2026-05-15

文章信息/Info

Title:
Analysis of Risk Factors for Distant Metastasis in Female Lung Cancer Patients Basedon the SEER Database
文章编号:
1006-1959（2026）10-0040-07
作者:
陈奕殷; 陈 鸿; 吴聪莲
福建医科大学附属泉州第一医院检验科，福建 泉州 362000
Author(s):
CHEN Yiyin;  CHEN Hong;  WU Conglian
Department of Clinical Laboratory, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, Fujian, China
关键词:
肺癌; 远处转移; 列线图
Keywords:
Lung cancer;  Distant metastasis;  Nomogram
分类号:
R734.2
DOI:
10.3969/j.issn.1006-1959.2026.10.006
文献标志码:
A
摘要:
目的 基于SEER数据库构建女性肺癌远处转移的预测模型，并验证其临床效能。方法 纳入SEER数据库中15 126例女性肺癌患者，随机分为训练集（10 588例）和验证集（4538例）。通过单因素及多因素逻辑回归筛选危险因素，构建预测远处转移的列线图，并通过受试者工作特征曲线下面积（AUC）评估其区分度，通过校准曲线评估其校准性能，通过决策曲线分析（DCA）评估其临床实用性。结果 有24.43%（3696例）患者在诊断时存在远处转移。多因素分析显示，非白人及黑人种族（OR=1.278，95%CI：1.071~1.527）、T2分期（OR=2.172，95%CI：1.946~2.426）、T3分期（OR=4.154，95%CI：3.678~4.696）、T4分期（OR=8.222，95%CI：7.253~9.328）、N1分期（OR=1.944，95%CI：1.721~2.195）、N2分期（OR=4.385，95%CI：4.021~4.784）、N3分期（OR=8.309，95%CI：7.267~9.510）、低分化肿瘤（Ⅲ~Ⅳ级，OR=1.699，95%CI：1.572~1.835）、肿瘤原发部位为重叠病变（OR=0.660，95%CI：0.471~0.915）及肿瘤大小（中等体积OR=1.173，95%CI：1.072~1.283）为独立影响因素（P＜0.05）。列线图在训练集和验证集的ROC曲线下面积分别为0.841和0.833，均大于单个危险因素。通过验证，校准曲线反映模型预测风险与实际发生风险具有一致性，且在大部分范围内该列线图模型有更高的临床净收益。结论 整合T/N分期、病理分级、种族、肿瘤大小及原发部位的诊断列线图具有高预测效能，可为女性肺癌患者远处转移风险评估提供量化工具，且区分度优于单一因素分析。
Abstract:
Objective To construct a prediction model for distant metastasis of female lung cancer based on SEER database and verify its clinical efficacy. Methods A total of 15 126 female lung cancer patients were included in the SEER database and randomly divided into training set (10 588 patients) and validation set (4538 patients). The risk factors were screened by univariate and multivariate logistic regression to construct a nomogram for predicting distant metastasis. The discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), the calibration performance was evaluated by the calibration curve, and the clinical practicability was evaluated by decision curve analysis (DCA). Results 24.43% (3696 patients) of patients had distant metastasis at the time of diagnosis. Multivariate analysis showed that non-white and non-black race (OR=1.278, 95%CI: 1.071-1.527), T2 stage (OR=2.172, 95%CI: 1.946-2.426), T3 stage (OR=4.154, 95%CI: 3.678-4.696 ), T4 stage (OR=8.222, 95%CI: 7.253-9.328), N1 stage (OR=1.944, 95%CI: 1.721-2.195), N2 stage (OR=4.385, 95%CI: 4.021-4.784), N3 stage (OR=8.309, 95%CI: 7.267-9.510), poorly differentiated tumor (grade Ⅲ-Ⅳ, OR=1.699, 95%CI: 1.572-1.835), the primary site of tumor was overlapping lesion (OR=0.660, 95%CI: 0.471-0.915), tumor size (medium volume, OR=1.173, 95%CI: 1.072-1.283) were independent influencing factors (P＜0.05). The area under the ROC curve of the nomogram in the training set and the validation set was 0.841 and 0.831, respectively, which was greater than that of a single risk factor. Through verification, the calibration curve reflects that the predicted risk of the model is consistent with the actual risk, and the nomogram model has higher clinical net income in most ranges. Conclusion The diagnostic nomogram integrating T/N stage, pathological grade, race, tumor size and primary site has high predictive efficacy, which can provide a quantitative tool for the risk assessment of distant metastasis in female lung cancer patients, and the discrimination is better than that of single factor analysis.

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更新日期/Last Update: 1900-01-01