"Gleason score, surgical and distant metastasis are associated with cancer-specific survival and overall survival in middle aged high-risk prostate cancer: A population-based study"

作者全名："Cao, Guangbiao; Li, Yan; Wang, Jinkui; Wu, Xin; Zhang, Zhaoxia; Zhanghuang, Chenghao; Han, Keqiang"

作者地址："[Cao, Guangbiao; Han, Keqiang] Songshan Gen Hosp, Dept Hepatobiliary Surg, Chongqing, Peoples R China; [Li, Yan] Childrens Hosp Chongqing Med Univ, Dept Stomatol, Chongqing, Peoples R China; [Wang, Jinkui; Wu, Xin; Zhang, Zhaoxia; Zhanghuang, Chenghao] Childrens Hosp Chongqing Med Univ, Dept Urol, Chongqing, Peoples R China"

通信作者："Han, KQ (通讯作者)，Songshan Gen Hosp, Dept Hepatobiliary Surg, Chongqing, Peoples R China."

来源：FRONTIERS IN PUBLIC HEALTH

ESI学科分类：SOCIAL SCIENCES, GENERAL

WOS号：WOS:000878517300001

JCR分区：Q1

影响因子：5.2

年份：2022

卷号：10

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：prostate cancer; middle age; high-risk; SEER; nomogram

摘要："ObjectiveAccording to statistics, patients with high-risk prostate cancer (PC) account for about 15% of prostate cancer diagnoses, and high-risk patients usually have a poor prognosis due to metastasis and recurrence and have a high mortality rate. Therefore, the accurate prediction of prognostic-related risk factors in middle-aged high-risk PC patients between 50 and 65 can help reduce patient mortality. We aimed to construct new nomograms for predicting cancer-specific survival (CSS) and Overall survival (OS) in middle-aged high-risk PC patients. MethodsData for patients aged between 50 and 65 years old and diagnosed with high-risk PC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for CSS and OS in patients. Nomograms predicting CSS and OS were developed based on multivariate Cox regression models. The concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and the calibration curve are used to detect the accuracy and discrimination of the model. Decision curve analysis (DCA) is used to detect the potential clinical value of this model. ResultsBetween 2010 and 2018, 1,651 patients diagnosed with high-risk PC and aged 50-65 years were included. In this study, the training group (n = 1,146) and the validation group (n = 505) were randomly assigned in a ratio of 7:3. The results showed that M stage, Gleason (GS) and surgical mode were independent risk factors for CSS; marital status, T stage, M stage, surgical mode, and GS were independent risk factors for OS. The C-index for predicting CSS in the training and validation groups are 0.84 and 0.811, respectively; the C-index for predicting OS in the training and validation groups are 0.824 and 0.784, respectively. The AUC and the calibration curves also showed good accuracy and discrimination. ConclusionsWe constructed new nomograms to predict CSS and OS in middle-aged high-risk PC patients. The prediction tools showed good accuracy and reliability, which can help clinicians and patients to make better clinical decisions."

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