PDF(Pubmed)
Abstract：
OBJECTIVE: Prostate cancer (PC) is a significant disease affecting men\'s health worldwide. More than 60% of patients over 65 years old and more than 80% are diagnosed with localized PC. The current choice of treatment modalities for localized PC and whether overtreatment is controversial. Therefore, we wanted to construct a nomogram to predict the risk factors associated with cancer-specific survival (CSS) and overall survival (OS) in elderly patients with localized PC while assessing the survival differences in surgery and radiotherapy for elderly patients with localized PC.
METHODS: Data of patients with localized PC over 65 years were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for CSS and OS. Nomograms predicting CSS and OS were built using multivariate Cox regression models. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to test the potential clinical value of this model.
RESULTS: A total of 90,434 patients over 65 years and diagnosed with localized PC from 2010 to 2018 were included in the study. All patients were randomly assigned to the training set (n = 63,328) and the validation set (n = 27,106). Univariate and multivariate Cox regression model analysis showed that age, race, marriage, T stage, surgical, radiotherapy, prostate-specific antigen (PSA), and Gleason score (GS) were independent risk factors for predicting CSS in elderly patients with localized PC. Age, race, marriage, surgery, radiotherapy, PSA, and GS were independent risk factors for predicting OS in elderly patients with localized PC. The c-index of the training and validation sets for the predicted CSS is 0.802(95%CI:0.788-0.816) and 0.798(95%CI:0.776-0.820, respectively). The c-index of the training and validation sets for predicting OS is 0.712(95%:0.704-0.720) and 0.724(95%:0.714-0.734). It shows that the nomograms have excellent discriminatory ability. The AUC and the calibration curves also show good accuracy and discriminability.
CONCLUSIONS: We have developed new nomograms to predict CSS and OS in elderly patients with localized PC. After internal validation and external temporal validation with reasonable accuracy, reliability and potential clinical value, the model can be used for clinically assisted decision-making.
METHODS: Data of patients with localized PC over 65 years were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for CSS and OS. Nomograms predicting CSS and OS were built using multivariate Cox regression models. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to test the potential clinical value of this model.
RESULTS: A total of 90,434 patients over 65 years and diagnosed with localized PC from 2010 to 2018 were included in the study. All patients were randomly assigned to the training set (n = 63,328) and the validation set (n = 27,106). Univariate and multivariate Cox regression model analysis showed that age, race, marriage, T stage, surgical, radiotherapy, prostate-specific antigen (PSA), and Gleason score (GS) were independent risk factors for predicting CSS in elderly patients with localized PC. Age, race, marriage, surgery, radiotherapy, PSA, and GS were independent risk factors for predicting OS in elderly patients with localized PC. The c-index of the training and validation sets for the predicted CSS is 0.802(95%CI:0.788-0.816) and 0.798(95%CI:0.776-0.820, respectively). The c-index of the training and validation sets for predicting OS is 0.712(95%:0.704-0.720) and 0.724(95%:0.714-0.734). It shows that the nomograms have excellent discriminatory ability. The AUC and the calibration curves also show good accuracy and discriminability.
CONCLUSIONS: We have developed new nomograms to predict CSS and OS in elderly patients with localized PC. After internal validation and external temporal validation with reasonable accuracy, reliability and potential clinical value, the model can be used for clinically assisted decision-making.
摘要：
目的：前列腺癌（PC）是影响全球男性健康的重要疾病。超过60%的65岁以上的患者和超过80%的患者被诊断为局部PC。目前局部PC的治疗方式选择以及过度治疗是否存在争议。因此,我们希望构建一个列线图来预测局部PC老年患者的癌症特异性生存率(CSS)和总生存率(OS)相关危险因素,同时评估局部PC老年患者手术和放疗的生存率差异.
方法：从监测中获得65岁以上局限性PC患者的数据，流行病学,和结束结果(SEER)数据库。使用单变量和多变量Cox回归模型来确定CSS和OS的独立危险因素。使用多变量Cox回归模型构建预测CSS和OS的列线图。一致性指数(C指数)，受试者工作特性曲线下的面积(AUC)，和校准曲线用于检验预测模型的准确性和区分度。使用决策曲线分析(DCA)来测试该模型的潜在临床价值。
结果：从2010年到2018年，共有90,434名65岁以上的患者被诊断为局部PC。将所有患者随机分配到训练集(n=63,328)和验证集(n=27,106)。单因素和多因素Cox回归模型分析表明,种族,婚姻,T级,外科,放射治疗,前列腺特异性抗原（PSA），Gleason评分(GS)是预测老年局限性PC患者CSS的独立危险因素。年龄,种族,婚姻,手术,放射治疗,PSA,GS和GS是预测老年局限性PC患者OS的独立危险因素。预测的CSS的训练和验证集的c指数分别为0.802(95CI:0.788-0.816)和0.798(95CI:0.776-0.820)。用于预测OS的训练和验证集的c指数为0.712(95%:0.704-0.720)和0.724(95%:0.714-0.734)。这表明列线图具有良好的判别能力。AUC和校准曲线也显示出良好的准确性和可辨别性。
结论：我们开发了新的列线图来预测患有局部PC的老年患者的CSS和OS。经过内部验证和外部时间验证，具有合理的准确性，可靠性和潜在临床价值，该模型可用于临床辅助决策。
方法：从监测中获得65岁以上局限性PC患者的数据，流行病学,和结束结果(SEER)数据库。使用单变量和多变量Cox回归模型来确定CSS和OS的独立危险因素。使用多变量Cox回归模型构建预测CSS和OS的列线图。一致性指数(C指数)，受试者工作特性曲线下的面积(AUC)，和校准曲线用于检验预测模型的准确性和区分度。使用决策曲线分析(DCA)来测试该模型的潜在临床价值。
结果：从2010年到2018年，共有90,434名65岁以上的患者被诊断为局部PC。将所有患者随机分配到训练集(n=63,328)和验证集(n=27,106)。单因素和多因素Cox回归模型分析表明,种族,婚姻,T级,外科,放射治疗,前列腺特异性抗原（PSA），Gleason评分(GS)是预测老年局限性PC患者CSS的独立危险因素。年龄,种族,婚姻,手术,放射治疗,PSA,GS和GS是预测老年局限性PC患者OS的独立危险因素。预测的CSS的训练和验证集的c指数分别为0.802(95CI:0.788-0.816)和0.798(95CI:0.776-0.820)。用于预测OS的训练和验证集的c指数为0.712(95%:0.704-0.720)和0.724(95%:0.714-0.734)。这表明列线图具有良好的判别能力。AUC和校准曲线也显示出良好的准确性和可辨别性。
结论：我们开发了新的列线图来预测患有局部PC的老年患者的CSS和OS。经过内部验证和外部时间验证，具有合理的准确性，可靠性和潜在临床价值，该模型可用于临床辅助决策。
