UNASSIGNED:可以在预期寿命的指导下进行个性化治疗策略,因此,生存预测很重要。尽管如此,在原发灶不明(CUP)的骨转移患者中,可靠的生存率评估仍然缺乏.该研究的目的是构建模型和基于网络的计算器,以使用基于机器学习的技术预测CUP骨转移患者的三个月死亡率。
UASSIGNED:这项研究从大型肿瘤数据库中纳入了1010名患者,监视,流行病学,和最终结果(SEER)数据库,2010年至2018年在美国。将整个患者群体随机分为两个队列:训练队列(n=600,60%)和验证队列(410,40%)。来自验证队列的患者在使用随机森林的四种机器学习方法开发模型后被用来验证模型,梯度增压机,决策树,和eXGBoosting机器对来自训练队列的患者。此外,来自两家大型教学医院的101名患者作为外部验证队列。为了评估每个模型预测结果的能力,预测措施,如接收器工作特性(AUROC)曲线下的面积,准确度,和Youden索引生成。该研究的风险分层是使用最佳临界值进行的。Streamlit软件用于建立基于网络的计算器。
未经证实:在整个队列中,3个月死亡率为72.38%(731/1010)。多因素分析显示年龄较大(P=0.031),肺转移(P=0.012),和肝转移(P=0.008)是三个月死亡率的危险因素,放疗(P=0.002)和化疗(P<0.001)是保护因素。随机森林模型显示曲线下面积(AUC)值最高(0.796,95%CI:0.746-0.847),第二高的精度(0.876)和准确度(0.778),和最高的尤登指数(1.486),与其他三种机器学习方法相比。根据外部验证队列,AUC值为0.748(95%CI:0.653-0.843),准确性为0.745。基于随机森林模型,建立了一个Web计算器:https://starxueshu-codeok-main-8jv2ws。streamlitapp.com/.与低风险组的患者相比,在内部验证队列中,高风险组患者在3个月内死亡的机率高1.99倍,在外部验证队列中死亡的机率高2.37倍(两者P<0.001).
UNASSIGNED:随机森林模型具有良好的辨别和校准性能。这项研究建议使用基于随机森林模型的基于网络的计算器来估计CUP骨转移的三个月死亡率,它可能是指导临床决策的有用工具,告知患者他们的预后,并促进患者和医生之间的治疗沟通。
UNASSIGNED: Individualized therapeutic strategies can be carried out under the guidance of expected lifespan, hence survival prediction is important. Nonetheless, reliable survival estimation in individuals with bone metastases from cancer of unknown primary (CUP) is still scarce. The objective of the
study is to construct a model as well as a web-based calculator to predict three-month mortality among bone metastasis patients with CUP using machine learning-based techniques.
UNASSIGNED: This
study enrolled 1010 patients from a large oncological database, the Surveillance, Epidemiology, and End Results (SEER) database, in the United States between 2010 and 2018. The entire patient population was classified into two cohorts at random: a training cohort (n=600, 60%) and a validation cohort (410, 40%). Patients from the validation cohort were used to validate models after they had been developed using the four machine learning approaches of random forest, gradient boosting machine, decision tree, and eXGBoosting machine on patients from the training cohort. In addition, 101 patients from two large teaching hospital were served as an external validation cohort. To evaluate each model\'s ability to predict the outcome, prediction measures such as area under the receiver operating characteristic (AUROC) curves, accuracy, and Youden index were generated. The
study\'s risk stratification was done using the best cut-off value. The Streamlit software was used to establish a web-based calculator.
UNASSIGNED: The three-month mortality was 72.38% (731/1010) in the entire cohort. The multivariate analysis revealed that older age (P=0.031), lung metastasis (P=0.012), and liver metastasis (P=0.008) were risk contributors for three-month mortality, while radiation (P=0.002) and chemotherapy (P<0.001) were protective factors. The random forest model showed the highest area under curve (AUC) value (0.796, 95% CI: 0.746-0.847), the second-highest precision (0.876) and accuracy (0.778), and the highest Youden index (1.486), in comparison to the other three machine learning approaches. The AUC value was 0.748 (95% CI: 0.653-0.843) and the accuracy was 0.745, according to the external validation cohort. Based on the random forest model, a web calculator was established: https://starxueshu-codeok-main-8jv2ws.streamlitapp.com/. When compared to patients in the low-risk groups, patients in the high-risk groups had a 1.99 times higher chance of dying within three months in the internal validation cohort and a 2.37 times higher chance in the external validation cohort (Both P<0.001).
UNASSIGNED: The random forest model has promising performance with favorable discrimination and calibration. This
study suggests a web-based calculator based on the random forest model to estimate the three-month mortality among bone metastases from CUP, and it may be a helpful tool to direct clinical decision-making, inform patients about their prognosis, and facilitate therapeutic communication between patients and physicians.