PDF(Pubmed)
Abstract：
Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects.
Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.
Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.
The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.
The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.
Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.
Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.
The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.
The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.
摘要：
研究表明，约30%的肾癌患者会发生转移，淋巴结转移（LNM）可能与预后不良有关。我们的回顾性研究旨在提供一种可靠的基于机器学习的模型来预测肾癌中LNM的发生。我们筛选了病理分级，肝转移,M分期，主站点,T分期,来自SEER数据库组成的训练组（n=39016）和医疗中心组成的验证组（n=771）的肿瘤大小。癌症患者LNM的独立预测因子。使用六种不同的算法建立预测模型,发现XGB模型在训练组和验证组中的预测性能明显优于其他任何机器学习模型。结果表明,基于机器学习的预测工具能够准确预测肾癌患者发生LNM的概率,具有令人满意的临床应用前景。
淋巴结转移（LNM）与肾癌患者的预后相关。这项研究旨在提供可靠的基于机器学习（基于ML）的模型来预测肾癌中LNM的概率。
从监测中提取诊断为肾癌的患者的数据，2010年至2017年的流行病学和结果（SEER）数据库，并通过最小绝对收缩和选择运算符（LASSO）过滤变量，单变量和多变量逻辑回归分析。使用具有统计学意义的风险因素来建立预测模型。我们在模型的验证中使用了10倍交叉验证。接收器工作特征曲线下面积(AUC)用于评估模型的性能。相关热图用于使用置换分析来研究特征的相关性，以评估预测因子的重要性。概率密度函数（PDF）和临床效用曲线（CUC）用于确定临床效用阈值。
这项研究的训练队列包括39,016名患者，验证队列包括771例患者.在这两个队列中，2544（6.5％）和66（8.1％）患者患有LNM，分别。病理分级,肝转移,M阶段,主站点,T级,肿瘤大小是LNM的独立预测因素。在两个模型验证中，XGB模型的AUC值为0.916,显著优于任何机器学习模型.Web计算器（https://share。streamlite.io/liuwencai4/肾_lnm/主/肾_lnm。py)是基于XGB模型构建的。基于PDF和CUC，我们建议54.6%作为指导LNM诊断的阈值概率，这可以区分大约89%的LNM患者。
基于机器学习的预测工具能够准确指示肾癌患者发生LNM的概率，在临床实践中具有良好的应用前景。
淋巴结转移（LNM）与肾癌患者的预后相关。这项研究旨在提供可靠的基于机器学习（基于ML）的模型来预测肾癌中LNM的概率。
从监测中提取诊断为肾癌的患者的数据，2010年至2017年的流行病学和结果（SEER）数据库，并通过最小绝对收缩和选择运算符（LASSO）过滤变量，单变量和多变量逻辑回归分析。使用具有统计学意义的风险因素来建立预测模型。我们在模型的验证中使用了10倍交叉验证。接收器工作特征曲线下面积(AUC)用于评估模型的性能。相关热图用于使用置换分析来研究特征的相关性，以评估预测因子的重要性。概率密度函数（PDF）和临床效用曲线（CUC）用于确定临床效用阈值。
这项研究的训练队列包括39,016名患者，验证队列包括771例患者.在这两个队列中，2544（6.5％）和66（8.1％）患者患有LNM，分别。病理分级,肝转移,M阶段,主站点,T级,肿瘤大小是LNM的独立预测因素。在两个模型验证中，XGB模型的AUC值为0.916,显著优于任何机器学习模型.Web计算器（https://share。streamlite.io/liuwencai4/肾_lnm/主/肾_lnm。py)是基于XGB模型构建的。基于PDF和CUC，我们建议54.6%作为指导LNM诊断的阈值概率，这可以区分大约89%的LNM患者。
基于机器学习的预测工具能够准确指示肾癌患者发生LNM的概率，在临床实践中具有良好的应用前景。
