PDF(Pubmed)
Abstract：
UNASSIGNED: This study aims to assess the effectiveness of the Gradient Boosting (GB) algorithm on glioma prognosis prediction and to explore new predictive models for glioma patient survival after tumor resection.
UNASSIGNED: A cohort of 776 glioma cases (WHO grades II-IV) between 2010 and 2017 was obtained. Clinical characteristics and biomarker information were reviewed. Subsequently, we constructed the conventional Cox survival model and three different supervised machine learning models, including support vector machine (SVM), random survival forest (RSF), Tree GB, and Component GB. Then, the model performance was compared with each other. At last, we also assessed the feature importance of models.
UNASSIGNED: The concordance indexes of the conventional survival model, SVM, RSF, Tree GB, and Component GB were 0.755, 0.787, 0.830, 0.837, and 0.840, respectively. All areas under the cumulative receiver operating characteristic curve of both GB models were above 0.800 at different survival times. Their calibration curves showed good calibration of survival prediction. Meanwhile, the analysis of feature importance revealed Karnofsky performance status, age, tumor subtype, extent of resection, and so on as crucial predictive factors.
UNASSIGNED: Gradient Boosting models performed better in predicting glioma patient survival after tumor resection than other models.
UNASSIGNED: A cohort of 776 glioma cases (WHO grades II-IV) between 2010 and 2017 was obtained. Clinical characteristics and biomarker information were reviewed. Subsequently, we constructed the conventional Cox survival model and three different supervised machine learning models, including support vector machine (SVM), random survival forest (RSF), Tree GB, and Component GB. Then, the model performance was compared with each other. At last, we also assessed the feature importance of models.
UNASSIGNED: The concordance indexes of the conventional survival model, SVM, RSF, Tree GB, and Component GB were 0.755, 0.787, 0.830, 0.837, and 0.840, respectively. All areas under the cumulative receiver operating characteristic curve of both GB models were above 0.800 at different survival times. Their calibration curves showed good calibration of survival prediction. Meanwhile, the analysis of feature importance revealed Karnofsky performance status, age, tumor subtype, extent of resection, and so on as crucial predictive factors.
UNASSIGNED: Gradient Boosting models performed better in predicting glioma patient survival after tumor resection than other models.
摘要：
UNASSIGNED：本研究旨在评估GradientBoosting（GB）算法对神经胶质瘤预后预测的有效性，并探索肿瘤切除后神经胶质瘤患者生存的新预测模型。
UNASSIGNED：获得了2010年至2017年776例神经胶质瘤病例（WHOII-IV级）的队列。回顾了临床特征和生物标志物信息。随后，我们构建了传统的Cox生存模型和三种不同的监督机器学习模型，包括支持向量机(SVM)，随机生存森林(RSF)，树GB,组件GB。然后，对模型的性能进行了比较。最后,我们还评估了模型的特征重要性.
未经评估：常规生存模型的一致性指标，SVM,RSF,树GB,组分GB分别为0.755、0.787、0.830、0.837和0.840。在不同的生存时间，两个GB模型的累积接收器工作特征曲线下的所有面积均大于0.800。它们的校准曲线显示了对生存预测的良好校准。同时,对特征重要性的分析揭示了Karnofsky的性能状态，年龄,肿瘤亚型,切除范围,等等作为关键的预测因素。
UNASSIGNED：梯度增强模型在预测胶质瘤患者肿瘤切除后的生存率方面比其他模型表现更好。
UNASSIGNED：获得了2010年至2017年776例神经胶质瘤病例（WHOII-IV级）的队列。回顾了临床特征和生物标志物信息。随后，我们构建了传统的Cox生存模型和三种不同的监督机器学习模型，包括支持向量机(SVM)，随机生存森林(RSF)，树GB,组件GB。然后，对模型的性能进行了比较。最后,我们还评估了模型的特征重要性.
未经评估：常规生存模型的一致性指标，SVM,RSF,树GB,组分GB分别为0.755、0.787、0.830、0.837和0.840。在不同的生存时间，两个GB模型的累积接收器工作特征曲线下的所有面积均大于0.800。它们的校准曲线显示了对生存预测的良好校准。同时,对特征重要性的分析揭示了Karnofsky的性能状态，年龄,肿瘤亚型,切除范围,等等作为关键的预测因素。
UNASSIGNED：梯度增强模型在预测胶质瘤患者肿瘤切除后的生存率方面比其他模型表现更好。
