Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer.
Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer.
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DOI：
10.3389/fonc.2021.731905
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发表时间：
2021
影响因子：
4.7
通讯作者：
Tian K
中科院分区：
文献类型：
--
作者：
Dong S;Yang H;Tang ZR;Ke Y;Wang H;Li W;Tian K
骨是肾癌转移的常见靶点，准确预测骨转移风险有助于肾癌的风险分层和精准医疗。从监测、流行病学和最终结果（SEER）数据库中提取诊断为肾癌的患者，组成2010年至2017年的训练组，验证组来自我们的学术医疗中心。单变量和多变量逻辑回归分析探讨了纳入变量与BM之间的统计关系。应用统计学上显著的危险因素形成nomogram。采用校正图、受试者工作特征（ROC）曲线、概率密度函数（PDF）和临床效用曲线（CUC）验证预测效果。Kaplan-Meier （KM）曲线显示了有脑转移和没有脑转移的两个肾癌亚组之间的生存差异。一个方便的网络计算器通过“闪亮”包提供给用户。本研究共招募43503例患者，其中训练组42650例，验证组853例。变量包括性别、病理分级、t分期、n分期、序列数、脑转移、肝转移、肺转移、组织学类型、原发部位和侧边性。标定图证实预测模型与实际结果吻合较好。训练组和验证组的曲线下面积（AUC）值分别为0.952 （95% CI, 0.950-0.954）和0.836 （95% CI, 0.809-0.860）。基于CUC，我们推荐阈值概率为5%来指导脑转移的诊断。由nomogram和网络计算器组成的综合预测工具有助于风险分层，帮助临床医生识别高危病例并提供个性化的治疗方案。
Bone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer. Patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package. A total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs. The comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.
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影响因子：
5.3
作者：
Lin, Patrick P.;Mirza, Attiqa N.;Yasko, Alan W.
通讯作者：
Yasko, Alan W.
DOI：
10.1097/01.blo.0000059580.08469.3e
发表时间：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
23.4
作者：
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