Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques.
Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques.
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DOI：
10.3389/fonc.2022.1095059
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发表时间：
2022
影响因子：
4.7
通讯作者：
Wang, Bailin
中科院分区：
文献类型：
--
作者：
Cui, Yunpeng;Wang, Qiwei;Shi, Xuedong;Ye, Qianwen;Lei, Mingxing;Wang, Bailin
个体化治疗策略可以在预期寿命的指导下进行，因此生存预测是重要的。尽管如此，可靠的生存估计在个人与骨转移癌的原发不明（CUP）仍然是稀缺的。该研究的目的是构建一个模型以及基于网络的计算器，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。这项研究从2010年至2018年期间美国的大型肿瘤数据库监测，流行病学和最终结果（SEER）数据库中招募了1010名患者。整个患者人群随机分为两个队列：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、梯度增强机、决策树和eXGBoosting机这四种机器学习方法对来自训练队列的患者开发模型后，用于验证模型。此外，来自两家大型教学医院的101例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，生成了预测指标，如受试者工作特征（AUROC）曲线下面积、准确性和Youden指数。该研究的风险分层使用最佳临界值进行。Streamlit软件被用来建立一个基于网络的计算器。全组3个月病死率为72.38%（731/1010）。多因素分析显示，高龄（P=0.031）、肺转移（P=0.012）和肝转移（P=0.008）是3个月死亡率的危险因素，而放疗（P=0.002）和化疗（P<0.001）是保护因素。与其他三种机器学习方法相比，随机森林模型显示出最高的曲线下面积（AUC）值（0.796，95% CI：0.746-0.847），第二高的精度（0.876）和准确度（0.778）以及最高的Youden指数（1.486）。根据外部验证队列，AUC值为0.748（95% CI：0.653-0.843），准确度为0.745。基于随机森林模型，建立了一个网络计算器：。与低风险组患者相比，在内部验证队列中，高风险组患者在3个月内死亡的可能性高1.99倍，在外部验证队列中高2.37倍（均P<0.001）。随机森林模型具有良好的识别和校准性能。本研究提出了一种基于网络的计算器，基于随机森林模型来估计CUP骨转移的3个月死亡率，它可能是一个有用的工具，以指导临床决策，告知患者他们的预后，并促进患者和医生之间的治疗沟通。
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. 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. 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: . 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). 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.
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影响因子：
4
作者：
Li X;Shao Y;Sheng L;Zhu J;Wang Z;Guo K;Sun L
通讯作者：
Sun L
在未知主要癌症中的个性化预测生存的个性化预测的新型诺明图的开发和验证。
DOI：
10.1158/1078-0432.ccr-20-4117
发表时间：
2021-06-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Raghav K;Hwang H;Jácome AA;Bhang E;Willett A;Huey RW;Dhillon NP;Modha J;Smaglo B;Matamoros A Jr;Estrella JS;Jao J;Overman MJ;Wang X;Greco FA;Loree JM;Varadhachary GR
通讯作者：
Varadhachary GR
影响因子：
2.9
作者：
Alim M;Ye GH;Guan P;Huang DS;Zhou BS;Wu W
通讯作者：
Wu W
DOI：
10.1007/978-1-4419-7046-6_19
发表时间：
2011-01-01
期刊：
SOFTWARE TOOLS AND ALGORITHMS FOR BIOLOGICAL SYSTEMS
影响因子：
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作者：
Che, Dongsheng;Liu, Qi;Tao, Xiuping
通讯作者：
Tao, Xiuping
DOI：
10.1080/13651501.2019.1619777
发表时间：
2019-05-22
影响因子：
3
作者：
Jia, Yong;Zhang, Wenlei;Chen, Li
通讯作者：
Chen, Li