PDF(Pubmed)
Abstract：
BACKGROUND: Small cell lung cancer (SCLC) is a highly aggressive lung cancer variant known for its elevated risk of brain metastases (BM). While earlier meta-analyses supported the use of prophylactic cranial irradiation (PCI) to reduce BM incidence and enhance overall survival, modern MRI capabilities raise questions about PCI\'s universal benefit for limited-stage SCLC (LS-SCLC) patients. As a response, we have created a predictive model for BM, aiming to identify low-risk individuals who may not require PCI.
METHODS: A total of 194 LS-SCLC patients without PCI treated between 2009 and 2021 were included. We conducted both univariate and multivariate analyses to pinpoint the factors associated with the development of BM. A nomogram for predicting the 2- and 3-year probabilities of BM was then constructed.
RESULTS: Univariate and multivariate analyses revealed several significant independent risk factors for the development of BM. These factors include TNM stage, the number of chemotherapy (ChT) cycles, Ki-67 expression level, pretreatment serum lactate dehydrogenase (LDH) levels, and haemoglobin (HGB) levels. These findings underscore their respective roles as independent predictors of BM. Based on the results of the final multivariable analysis, a nomogram model was created. In the training cohort, the nomogram yielded an area under the receiver operating characteristic curve (AUC) of 0.870 at 2 years and 0.828 at 3 years. In the validation cohort, the AUC values were 0.897 at 2 years and 0.789 at 3 years. The calibration curve demonstrated good agreement between the predicted and observed probabilities of BM.
CONCLUSIONS: A novel nomogram has been developed to forecast the likelihood of BM in patients diagnosed with LS-SCLC. This tool holds the potential to assist healthcare professionals in formulating more informed and tailored treatment plans.
METHODS: A total of 194 LS-SCLC patients without PCI treated between 2009 and 2021 were included. We conducted both univariate and multivariate analyses to pinpoint the factors associated with the development of BM. A nomogram for predicting the 2- and 3-year probabilities of BM was then constructed.
RESULTS: Univariate and multivariate analyses revealed several significant independent risk factors for the development of BM. These factors include TNM stage, the number of chemotherapy (ChT) cycles, Ki-67 expression level, pretreatment serum lactate dehydrogenase (LDH) levels, and haemoglobin (HGB) levels. These findings underscore their respective roles as independent predictors of BM. Based on the results of the final multivariable analysis, a nomogram model was created. In the training cohort, the nomogram yielded an area under the receiver operating characteristic curve (AUC) of 0.870 at 2 years and 0.828 at 3 years. In the validation cohort, the AUC values were 0.897 at 2 years and 0.789 at 3 years. The calibration curve demonstrated good agreement between the predicted and observed probabilities of BM.
CONCLUSIONS: A novel nomogram has been developed to forecast the likelihood of BM in patients diagnosed with LS-SCLC. This tool holds the potential to assist healthcare professionals in formulating more informed and tailored treatment plans.
摘要：
背景：小细胞肺癌（SCLC）是一种高度侵袭性的肺癌变体，以其脑转移（BM）的风险升高而闻名。虽然早期的荟萃分析支持使用预防性颅骨照射（PCI）来降低BM发生率并提高总生存率，现代MRI功能引发了关于有限阶段SCLC(LS-SCLC)患者PCI的普遍获益的疑问.作为回应，我们为BM创建了一个预测模型，旨在识别可能不需要PCI的低风险个体。
方法：纳入了2009年至2021年间未接受PCI治疗的194例LS-SCLC患者。我们进行了单变量和多变量分析，以查明与BM发展相关的因素。然后构造了用于预测BM的2年和3年概率的列线图。
结果：单变量和多变量分析揭示了发展BM的几个显著的独立危险因素。这些因素包括TNM阶段，化疗（ChT）周期数，Ki-67表达水平,预处理血清乳酸脱氢酶（LDH）水平，和血红蛋白（HGB）水平。这些发现强调了它们各自作为BM独立预测因子的作用。根据最终的多变量分析结果，创建了列线图模型。在训练组中，列线图显示,2年时受试者工作特征曲线下面积(AUC)为0.870,3年时为0.828.在验证队列中，2年和3年的AUC值分别为0.897和0.789.校准曲线表明BM的预测和观察到的概率之间具有良好的一致性。
结论：已经开发了一个新的列线图来预测诊断为LS-SCLC的患者发生BM的可能性。该工具具有协助医疗保健专业人员制定更知情和量身定制的治疗计划的潜力。
方法：纳入了2009年至2021年间未接受PCI治疗的194例LS-SCLC患者。我们进行了单变量和多变量分析，以查明与BM发展相关的因素。然后构造了用于预测BM的2年和3年概率的列线图。
结果：单变量和多变量分析揭示了发展BM的几个显著的独立危险因素。这些因素包括TNM阶段，化疗（ChT）周期数，Ki-67表达水平,预处理血清乳酸脱氢酶（LDH）水平，和血红蛋白（HGB）水平。这些发现强调了它们各自作为BM独立预测因子的作用。根据最终的多变量分析结果，创建了列线图模型。在训练组中，列线图显示,2年时受试者工作特征曲线下面积(AUC)为0.870,3年时为0.828.在验证队列中，2年和3年的AUC值分别为0.897和0.789.校准曲线表明BM的预测和观察到的概率之间具有良好的一致性。
结论：已经开发了一个新的列线图来预测诊断为LS-SCLC的患者发生BM的可能性。该工具具有协助医疗保健专业人员制定更知情和量身定制的治疗计划的潜力。
