肺癌是癌症死亡的主要原因,可手术的老年肺癌患者的数量正在增加,高龄与预后较差有关。然而,对于这些患者,目前尚无简便、全面的预后评估方法。
从SEER数据库下载2004年至2018年年龄在65岁或以上的TNMI-II期肺癌患者的临床病理数据。2004年至2015年的患者被随机分为训练组(n=16,457)和验证组(n=7,048)。2016年至2018年的数据(n=6,231)用于外部验证。通过使用单变量和多变量Cox比例风险回归分析,在训练集中与总生存率(OS)和癌症特异性生存率(CSS)相关的独立预后因素之后,创建了两个列线图预后模型。反过来,对患者的1,3和5年总生存期(OS)和癌症特异性生存期(CSS)进行了预测.根据一致性指数(C指数),校正曲线,接收器工作特性曲线(ROC)下面积(AUC),ROC曲线下的时间依赖性面积,有效性,准确度,歧视,预测能力,并对该模型的临床实用性进行了评价。使用决策曲线分析(DCA)来评估模型的临床价值。
共纳入29,736例患者。单变量和多变量分析表明,年龄,种族,性别,婚姻,疾病等级,AJCC阶段,T-stage,手术,放射治疗,化疗,肿瘤大小是影响患者预后的独立危险因素。这11个变量包括在列线图中以预测患者的OS和CSS。用于训练的操作系统的C索引,验证集和外部验证集为0.730(95%CI,0.709-0.751),0.734(95%CI,0.722-0.746),和0.750(95%CI,0.734-0.766),分别。训练集和验证集的AUC结果表明该列线图具有良好的准确性。校准曲线显示了实际值和预期值之间的高度一致性。DCA表明,列线图比传统的TNM分期方法具有更好的临床应用价值。
这项研究确定了可手术的老年肺癌患者生存的风险因素,并建立了一个新的列线图来预测这些患者的OS和CSS。该模型具有良好的临床应用价值,可为医师和患者提供良好的临床决策工具。
Lung cancer is the leading cause of death from cancer and the number of operable elderly lung cancer patients is increasing, with advanced age being associated with a poorer prognosis. However, there is no easy and comprehensive prognostic assessment method for these patients.
Clinicopathological data of patients aged 65 years or older with TNM stage I-II lung cancer from 2004 to 2018 were downloaded from the SEER database. Patients from 2004 to 2015 were randomized into a training group (n = 16,457) and a validation group (n = 7,048). Data from 2016 to 2018 (n = 6,231) were used for external validation. Two nomogram prognostic models were created after independent prognostic factors connected to both overall survival (OS) and cancer-specific survival (
CSS) in the training set by using univariate and multivariate Cox proportional hazards regression analysis. In turn, overall survival (OS) and cancer-specific survival (
CSS) were predicted for patients at 1, 3, and 5 years. Based on the concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve (AUC), the time-dependent area under the ROC curve, the validity, accuracy, discrimination, predictive ability, and clinical utility of the models were evaluated. Decision curve analysis (DCA) was used to assess the clinical value of the models.
A total of 29,736 patients were included. Univariate and multivariate analyses suggested that age, race, gender, marriage, disease grade, AJCC stage, T-stage, surgery, radiotherapy, chemotherapy, and tumor size were independent risk factors for patient prognosis. These 11 variables were included in nomogram to predict OS and
CSS of patients. C-indexes of OS for the training, validation and external validation sets were 0.730 (95% CI, 0.709-0.751), 0.734 (95% CI, 0.722-0.746), and 0.750 (95% CI, 0.734-0.766), respectively. The AUC results for the training and validation sets indicated good accuracy for this nomogram. The calibration curves demonstrated a high degree of concordance between actual and anticipated values, and the DCA demonstrated that the nomograms had better clinical application than the traditional TNM staging approach.
This
study identified risk factors for survival in operable elderly lung cancer patients and established a new column line graph for predicting OS and
CSS in these patients. The model has good clinical application and can be a good clinical decision-making tool for physicians and patients.