Construction of nomogram model of poor prognosis for patients newly diagnosed with brain metastasis from non-small cell lung cancer based on clinical pathology and prognostic scores.

IF 3.5 3区 医学 Q2 ONCOLOGY Frontiers in Oncology Pub Date : 2025-03-07 eCollection Date: 2025-01-01 DOI:10.3389/fonc.2025.1487126
Zengliang Li, Xiaoyue Wang, Guodong Ma
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Abstract

Objective: 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.

Methods: 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.

Results: 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 (P < 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 (P < 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.

Conclusion: 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.

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基于临床病理和预后评分的新诊断非小细胞肺癌脑转移患者预后不良的nomogram模型构建
目的:探讨非小细胞肺癌(NSCLC)患者脑转移的新诊断,并基于临床病理及预后评分构建Logistic回归模型,并进行验证。方法:回顾性选择2020年3月至2022年4月新诊断为NSCLC脑转移的患者158例。收集患者临床资料,采用Logistic回归分析新诊断NSCLC合并脑转移预后不良的影响因素。结果:单因素分析结果显示,NLR>2.94、CEA异常、膈淋巴结转移、对症治疗方法、颅外转移、GPS1-2评分等临床病理特征与新诊断NSCLC脑转移患者的生存及预后相关(P < 0.05)。多因素Logistic回归分析显示NLR>2.94、纵膈淋巴结转移、CEA异常、颅外转移、新诊断NSCLC GPS1-2评分为脑转移预后不良的独立危险因素(P < 0.05)。利用Bootstrap方法进行内部验证,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,相应的特异性为90.50%,敏感性为80.00%。这表明该模型的预测精度较好。结论:NLR、纵隔淋巴结转移、CEA、颅外转移和GPS是新诊断的NSCLC脑转移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的NSCLC脑转移预后不良具有很好的预测价值。为了降低NSCLC新诊断脑转移的风险,改善预后,临床针对上述危险因素采取有针对性的预防措施。
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来源期刊
Frontiers in Oncology
Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
6.20
自引率
10.60%
发文量
6641
审稿时长
14 weeks
期刊介绍: Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.
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