We performed simultaneous one-stage thoraciccranial surgery on ten cases of lung cancer with brain metastases during the period of 1990 to 1994. Surgical mortality was 0% with low morbidity. By the end of the follow-u...We performed simultaneous one-stage thoraciccranial surgery on ten cases of lung cancer with brain metastases during the period of 1990 to 1994. Surgical mortality was 0% with low morbidity. By the end of the follow-up in February 1995, 4 patients died, with a mean survival of 8.25 months, and 6 patients survived, with a mean survival of 16 months and the longest one being approximately 36 months. Our results showed that, if patient's general condition permits, simultaneous onestage thoraco-cranial operation is feasible for the treatment of lung cancer involved the Periphery with solitary intracranial metastasis. Postoperative adjuvant chemotherapy is indicated to achieve better results.展开更多
目的构建并验证一个模型以预测肺癌脑转移(lung cancer with brain metastases,LCBM)患者确诊后三个月内死亡的风险。方法本研究纳入监测,流行病学和最终结果(Surveillance,Epidemiology and End Results,SEER)数据库内2010年1月至2015...目的构建并验证一个模型以预测肺癌脑转移(lung cancer with brain metastases,LCBM)患者确诊后三个月内死亡的风险。方法本研究纳入监测,流行病学和最终结果(Surveillance,Epidemiology and End Results,SEER)数据库内2010年1月至2015年12月期间确诊为LCBM的患者。以是否发生早期死亡为研究终点将患者分为早期死亡组和非早期死亡组。以8∶2为比例将数据分为训练集和验证集。在训练集上采用最小绝对值收缩和筛选算子(least absolute shrinkage and selection operator,LASSO)回归法筛选预测因子,并使用多因素Logistic回归构建预测模型并创建列线图。使用受试者工作特征(receiver operating characteristic,ROC)曲线、校准曲线和临床决策曲线(decision curve analysis,DCA)分别在训练集和验证集上评估模型性能。结果共纳入5035例患者,早期死亡发生率28.3%。LASSO回归筛选出13个变量,Logistic回归最终保留了13个与LCBM患者早期死亡相关的危险因素,包括年龄、从诊断到开始治疗时间、肿瘤大小、肿瘤部位、肿瘤分化程度和组织学类型、T分期、N分期、手术、放疗、化疗、肝转移和骨转移。验证集的一致性指数(concordance index,C-index)为0.84,校准曲线和DCA显示模型具有较好的预测效能和临床净效益。结论基于多因素Logistic回归构建的LCBM患者发生早期死亡的预测模型的区分度较好,能够为临床决策提供一定的帮助。展开更多
文摘We performed simultaneous one-stage thoraciccranial surgery on ten cases of lung cancer with brain metastases during the period of 1990 to 1994. Surgical mortality was 0% with low morbidity. By the end of the follow-up in February 1995, 4 patients died, with a mean survival of 8.25 months, and 6 patients survived, with a mean survival of 16 months and the longest one being approximately 36 months. Our results showed that, if patient's general condition permits, simultaneous onestage thoraco-cranial operation is feasible for the treatment of lung cancer involved the Periphery with solitary intracranial metastasis. Postoperative adjuvant chemotherapy is indicated to achieve better results.
文摘目的构建并验证一个模型以预测肺癌脑转移(lung cancer with brain metastases,LCBM)患者确诊后三个月内死亡的风险。方法本研究纳入监测,流行病学和最终结果(Surveillance,Epidemiology and End Results,SEER)数据库内2010年1月至2015年12月期间确诊为LCBM的患者。以是否发生早期死亡为研究终点将患者分为早期死亡组和非早期死亡组。以8∶2为比例将数据分为训练集和验证集。在训练集上采用最小绝对值收缩和筛选算子(least absolute shrinkage and selection operator,LASSO)回归法筛选预测因子,并使用多因素Logistic回归构建预测模型并创建列线图。使用受试者工作特征(receiver operating characteristic,ROC)曲线、校准曲线和临床决策曲线(decision curve analysis,DCA)分别在训练集和验证集上评估模型性能。结果共纳入5035例患者,早期死亡发生率28.3%。LASSO回归筛选出13个变量,Logistic回归最终保留了13个与LCBM患者早期死亡相关的危险因素,包括年龄、从诊断到开始治疗时间、肿瘤大小、肿瘤部位、肿瘤分化程度和组织学类型、T分期、N分期、手术、放疗、化疗、肝转移和骨转移。验证集的一致性指数(concordance index,C-index)为0.84,校准曲线和DCA显示模型具有较好的预测效能和临床净效益。结论基于多因素Logistic回归构建的LCBM患者发生早期死亡的预测模型的区分度较好,能够为临床决策提供一定的帮助。
文摘目的探讨表观扩散系数(apparent diffusion coefficient,ADC)鉴别诊断肺癌脑转移瘤组织学分型的价值及其与Ki-67增殖指数之间的关系。材料与方法回顾性分析经手术病理证实的20例小细胞肺癌脑转移瘤和41例非小细胞肺癌脑转移瘤患者的资料,并测定其Ki-67增殖指数。在ADC图上测量肿瘤实性部分的最小ADC值(the minimum ADC,ADCmin)、平均ADC值(the mean ADC,ADCmean)及对侧正常脑白质ADC值,并计算相对ADCmin(relative ADCmin,rADCmin)及相对ADCmean(relative ADCmean,rADCmean)。对比分析二者ADC值的差异,绘制受试者工作特征(receiver operating characteristic,ROC)曲线评价ADC值的鉴别诊断价值,并计算ADC值与Ki-67增殖指数之间的相关性。结果小细胞肺癌脑转移瘤组的ADCmin、ADCmean、rADCmin及rADCmean值均小于非小细胞肺癌脑转移瘤组,组间差异均具有统计学意义(P<0.05)。各ADC值均能对小细胞肺癌脑转移瘤及非小细胞肺癌脑转移瘤进行有效鉴别,其中rADCmean值的鉴别诊断效能最好,曲线下面积(area under the curve,AUC)为0.950[95%置信区间(confidence interval,CI):0.907~0.994],最佳截断值为0.955,相应的敏感度和特异度分别为96.23%、83.87%,准确度为91.67%。小细胞肺癌脑转移瘤组的Ki-67增殖指数大于非小细胞肺癌脑转移瘤组,组间差异具有统计学意义(P<0.05)。61例肺癌脑转移瘤患者的ADCmin、ADCmean、rADCmin及rADCmean值均与Ki-67增殖指数呈不同程度的负相关(r=-0.506、r=-0.480、r=-0.569、r=-0.541)。结论ADC值可以对肺癌脑转移瘤的组织学分型进行鉴别诊断,并可以预测Ki-67增殖指数的表达水平。
