Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population-Based Analysis.
Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population-Based Analysis.
复制标题
DOI：
10.3389/fendo.2021.752176
复制
发表时间：
2021
影响因子：
5.2
通讯作者：
Bi Q
中科院分区：
文献类型：
--
作者：
Zhang W;Ji L;Wang X;Zhu S;Luo J;Zhang Y;Tong Y;Feng F;Kang Y;Bi Q
胰腺癌（PC）合并骨转移（BM）患者的总生存期（OS）极低，骨转移的治疗相当困难。然而，目前还没有有效的影像学图来预测胰腺癌骨转移的诊断和预后。因此，建立有效的预测模型来指导临床实践具有重要意义。我们从2010年至2016年的监测流行病学和最终结果（SEER）数据库中筛选患者。通过单变量和多变量logistic回归分析确定PCBM的独立危险因素，并通过单变量和多变量Cox比例风险回归分析确定影响PCBM预后的独立预后因素。此外，我们还构建了两种形态图来预测PCBM的风险和预后。利用曲线下面积（AUC）、c指数和校准曲线来确定图的预测精度和判别性。采用决策曲线分析（DCA）和Kaplan-Meier生存曲线（K-M）进一步证实nomogram临床疗效。多变量logistic回归分析显示PCBM的危险因素包括年龄、原发部位、组织学亚型、N分期、放疗、手术、脑转移、肺转移、肝转移。通过Cox回归分析，我们发现PCBM的独立预后因素有年龄、种族、分级、组织学亚型、手术、化疗和肺转移。我们使用图来直观地表达数据分析结果。训练队列的c指数为0.795 (95%CI: 0.758 ~ 0.832)，内部验证队列的c指数为0.800 (95%CI: 0.739 ~ 0.862)，外部验证队列的c指数为0.787 （95%CI: 0.746 ~ 0.828）。基于受试者工作特征（ROC）分析、校正图和决策曲线分析（DCA）的AUC，我们认为PCBM的风险和预后模型具有良好的性能。Nomogram能够足够准确地预测PCBM的风险和预后因素，从而为今后的临床工作提供个性化的临床决策。
The overall survival (OS) of pancreatic cancer (PC) patients with bone metastasis (BM) is extremely low, and it is pretty hard to treat bone metastasis. However, there are currently no effective nomograms to predict the diagnosis and prognosis of pancreatic cancer with bone metastasis (PCBM). Therefore, it is of great significance to establish effective predictive models to guide clinical practice. We screened patients from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2016. The independent risk factors of PCBM were identified from univariable and multivariable logistic regression analyses, and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors affecting the prognosis of PCBM. In addition, two nomograms were constructed to predict the risk and prognosis of PCBM. We used the area under the curve (AUC), C-index and calibration curve to determine the predictive accuracy and discriminability of nomograms. The decision curve analysis (DCA) and Kaplan-Meier(K-M) survival curves were employed to further confirm the clinical effectiveness of the nomogram. Multivariable logistic regression analyses revealed that risk factors of PCBM included age, primary site, histological subtype, N stage, radiotherapy, surgery, brain metastasis, lung metastasis, and liver metastasis. Using Cox regression analyses, we found that independent prognostic factors of PCBM were age, race, grade, histological subtype, surgery, chemotherapy, and lung metastasis. We utilized nomograms to visually express data analysis results. The C-index of training cohort was 0.795 (95%CI: 0.758-0.832), whereas that of internal validation cohort was 0.800 (95%CI: 0.739-0.862), and the external validation cohort was 0.787 (95%CI: 0.746-0.828). Based on AUC of receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance. Nomogram is sufficiently accurate to predict the risk and prognostic factors of PCBM, allowing for individualized clinical decisions for future clinical work.
登录
查看更多内容
影响因子：
3.8
作者：
Huang Z;Hu C;Liu K;Yuan L;Li Y;Zhao C;Hu C
通讯作者：
Hu C
影响因子：
3.4
作者：
Guan Z;Lan H;Chen X;Jiang X;Wang X;Jin K
通讯作者：
Jin K
影响因子：
10.6
作者：
Lyons, JD;Alibazoglu, B;Hollinger, EF
通讯作者：
Hollinger, EF
影响因子：
5.2
作者：
Leber, Mathias Felix;Efferth, Thomas
通讯作者：
Efferth, Thomas
影响因子：
2.8
作者：
Han, Lingyu;Dai, Weixing;Cai, Guoxiang
通讯作者：
Cai, Guoxiang