| 马国玉,黄 瑾,杨 鑫,等.骨肉瘤患者肺转移影响因素分析及预测模型的构建与验证[J].肿瘤学杂志,2025,31(3):231-237. |
| 骨肉瘤患者肺转移影响因素分析及预测模型的构建与验证 |
| Construction and Validation Prediction Model for Risk of Lung Metastasis in Osteosarcoma Patients |
| 投稿时间：2024-07-18 |
| DOI：10.11735/j.issn.1671-170X.2025.03.B008 |
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| 中文关键词: 骨肉瘤 肺转移 影响因素 预测模型 |
| 英文关键词:osteosarcoma lung metastasis influencing factors prediction model |
| 基金项目:国家自然科学基金项目（81960488）；昆明医科大学骨肉瘤综合防治研究科技创新团队（CXTD202212） |
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| 中文摘要: |
| 摘 要： ［目的］ 分析骨肉瘤患者发生肺转移的危险因素，构建骨肉瘤患者肺转移Nomogram预测模型，为临床诊疗提供参考依据。［方法］ 收集2013年2月至2022年12月在云南省肿瘤医院首诊确诊骨肉瘤469例患者信息资料，男性和女性分别为296例和173例。 采用R语言（4.3.2版本）统计软件分析数据，通过单因素、多因素分析肺转移的独立危险因素。基于多因素分析结果构建预测模型，绘制列线图。采用校准曲线、决策曲线、临床影响曲线和受试者工作特征（receiver operating characteristic，ROC）曲线对模型进行验证。［结果］ 469例骨肉瘤患者中发生肺转移160例，肺转移率34.1%。多因素Logistic回归分析结果显示：Enneking手术分期Ⅲ期、低中性粒细胞、高尿素氮和高乳酸脱氢酶是骨肉瘤患者肺转移的独立危险因素。 基于4个危险因素建立预测模型ROC曲线下面积为0.775（95%CI：0.726～0.824），表明模型的预测效能较好。［结论］ 临床医生在临床诊疗过程中，应关注骨肉瘤患者Enneking手术分期、中性粒细胞、尿素氮和乳酸脱氢酶，以更好地预测骨肉瘤患者发生肺转移的风险，预防或延迟患者发生肺转移，改善患者预后。 |
| 英文摘要: |
| Abstract： ［Objective］ To analyze the influencing factors of lung metastasis in patients with osteosarcoma， and to construct a Nomogram risk prediction model. ［Methods］ Clinical data of 469 patients with osteosarcoma treated in Yunnan Cancer Hospital from February 2013 to December 2022 were collected， included 296 males and 173 females. Software R (4.3.2) was adopted to analyze data and screen for independent influencing factors for lung metastasis through univariate and multivariate analysis. A Nomogram prediction model for lung metastasis risk in osteosarcoma patients was constructed. Calibration curves， decision curve and receiver operating characteristic curve were applied to evaluate the prediction model. ［Results］ Among 469 patients 160 experienced lung metastasis (34.1%). Multivariate Logistic regression analysis showed that Enneking’s surgical staging Ⅲ， decreased neutrophil， increased urea nitrogen and increased lactate dehydrogenase were independent risk factors for lung metastasis in osteosarcoma patients. Based on these risk factors a prediction Nomogram model was constructed. The area under curve of the prediction model was 0.775(95%CI： 0.726~0.824)， indicating that the model had a good predictive performance. ［Conclusion］ Clinicians should pay attention to the Enneking stage， neutrophils， urea nitrogen and lactate dehydrogenase in osteosarcoma patients, so as to better evaluate and predict the risk of lung metastasis in osteosarcoma patients， prevent or delay lung metastasis in patients， and improve patient prognosis. |
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