Li Zengliang, Wang Xiaoyue, Ma Guodong
Department of Thoracic Surgery, Nanjing Chest Hospital, Nanjing, China.
Department of Thoracic Surgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
Front Oncol. 2025 Mar 7;15:1487126. doi: 10.3389/fonc.2025.1487126. eCollection 2025.
To explore non-small cell lung cancer (NSCLC) patients with new diagnosis of brain metastasis and construct Logistic regression model based on clinical pathology and prognosis score, and verify.
A total of 158 patients newly diagnosed with brain metastasis in NSCLC were retrospectively selected from March 2020 to April 2022. The clinical data of patients were collected, and Logistic regression analysis was used to analyze the influencing factors of poor prognosis for newly diagnosed NSCLC with brain metastasis.
The results of univariate analysis showed that the clinical pathological features including NLR>2.94, abnormal CEA, mediastinal lymph node metastasis, symptomatic treatment with therapeutic method, extracranial metastasis and GPS1-2 score were associated with the survival and prognosis of patients with newly diagnosed brain metastasis from NSCLC ( < 0.05). Multivariate Logistic regression analysis showed that NLR>2.94, mediastinal lymph node metastasis, CEA abnormality, extracranial metastasis, and newly diagnosed NSCLC with GPS1-2 score were independent risk factors for poor prognosis of brain metastasis ( < 0.05). Internal verification using the Bootstrap method showed that the predicted curve fitted well with the standard model curve, with the average absolute error of 0.029. The ROC curve result showed that the AUC was 0.887, and the 95%CI was 0.782-0.905, with the corresponding specificity and sensitivity of 90.50% and 80.00%, respectively. This indicates that the prediction accuracy of this Nomogram model is good.
NLR, mediastinal lymph node metastasis, CEA, extracranial metastasis and GPS are risk factors for poor prognosis of newly diagnosed brain metastasis in NSCLC. The risk factor model constructed based on these risk factors has excellent prediction value for the poor prognosis of newly diagnosed brain metastasis in NSCLC. In order to reduce the risk of newly diagnosed brain metastasis in NSCLC and improve the prognosis, targeted preventive measures are taken against the above risk factors in clinical practice.
探讨新诊断为脑转移的非小细胞肺癌(NSCLC)患者,构建基于临床病理和预后评分的Logistic回归模型并进行验证。
回顾性选取2020年3月至2022年4月新诊断为脑转移的158例NSCLC患者。收集患者的临床资料,采用Logistic回归分析新诊断为脑转移的NSCLC患者预后不良的影响因素。
单因素分析结果显示,NLR>2.94、CEA异常、纵隔淋巴结转移、治疗方法为对症治疗、颅外转移及GPS1-2评分等临床病理特征与新诊断为脑转移的NSCLC患者的生存及预后相关(<0.05)。多因素Logistic回归分析显示,NLR>2.94、纵隔淋巴结转移、CEA异常、颅外转移及GPS1-2评分的新诊断为脑转移的NSCLC患者是预后不良的独立危险因素(<0.05)。采用Bootstrap法进行内部验证,结果显示预测曲线与标准模型曲线拟合良好,平均绝对误差为0.029。ROC曲线结果显示,AUC为0.887,95%CI为0.782-0.905,相应的特异性和敏感性分别为90.50%和80.00%。这表明该列线图模型的预测准确性良好。
NLR、纵隔淋巴结转移、CEA、颅外转移及GPS是新诊断为脑转移的NSCLC患者预后不良的危险因素。基于这些危险因素构建的危险因素模型对新诊断为脑转移的NSCLC患者预后不良具有良好的预测价值。为降低NSCLC新诊断为脑转移的风险并改善预后,临床实践中针对上述危险因素采取针对性预防措施。