Which model is better in predicting the survival of laryngeal squamous cell carcinoma?: Comparison of the random survival forest based on machine learning algorithms to Cox regression: analyses based on SEER database

比例危险模型 布里氏评分 医学 随机森林 生存分析 统计 预测建模 Lasso(编程语言) 回归 回归分析 多元统计 算法 肿瘤科 人工智能 内科学 数学 计算机科学 万维网
作者
Haili Sun,Shuangshuang Wu,Shaoxiao Li,Xiaohua Jiang
出处
期刊:Medicine [Wolters Kluwer]
卷期号:102 (10): e33144-e33144 被引量:1
标识
DOI:10.1097/md.0000000000033144
摘要

Prediction of postoperative survival for laryngeal carcinoma patients is very important. This study attempts to demonstrate the utilization of the random survival forest (RSF) and Cox regression model to predict overall survival of laryngeal squamous cell carcinoma (LSCC) and compare their performance. A total of 8677 patients diagnosed with LSCC from 2004 to 2015 were obtained from surveillance, epidemiology, and end results database. Multivariate imputation by chained equations was applied to filling the missing data. Lasso regression algorithm was conducted to find potential predictors. RSF and Cox regression were used to develop the survival prediction models. Harrell's concordance index (C-index), area under the curve (AUC), Brier score, and calibration plot were used to evaluate the predictive performance of the 2 models. For 3-year survival prediction, the C-index in training set were 0.74 (0.011) and 0.84 (0.013) for Cox and RSF respectively. For 5-year survival prediction, the C-index in training set were 0.75 (0.022) and 0.80 (0.011) for Cox and RSF respectively. Similar results were found in validation set. The AUC were 0.795 for RSF and 0.715 for Cox in the training set while the AUC were 0.765 for RSF and 0.705 for Cox in the validation set. The prediction error curves for each model based on Brier score showed the RSF model had lower prediction errors both in training group and validation group. What's more, the calibration curve displayed similar results of 2 models both in training set and validation set. The performance of RSF model were better than Cox regression model. The RSF algorithms provide a relatively better alternatives to be of clinical use for estimating the survival probability of LSCC patients.
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