基于机器学习的肾细胞癌肝转移风险预测的临床预测模型。

BACKGROUND: Renal cell carcinoma (RCC) is a highly metastatic urological cancer. RCC with liver metastasis (LM) carries a dismal prognosis. The objective of this study is to develop a machine learning (ML) model that predicts the risk of RCC with LM, which is used to assist clinical treatment. METHODS: The retrospective study data of 42,547 patients with RCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. ML includes algorithmic methods and is a fast-rising field that has been widely used in the biomedical field. Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), random forest (RF), decision tree (DT), and naive Bayesian model [Naive Bayes Classifier (NBC)] were applied to develop prediction models to predict the risk of RCC with LM. The six models were 10-fold cross-validated, and the best-performing model was selected based on the area under the curve (AUC) value. A web online calculator was constructed based on the best ML model. RESULTS: Bone metastasis, lung metastasis, grade, T stage, N stage, and tumor size were independent risk factors for the development of RCC with LM by multivariate regression analysis. In addition, the correlation of the relative proportions of the six clinical variables was shown by a heat map. In the prediction models of RCC with LM, the mean AUC of the XGB model among the six ML algorithms was 0.947. Based on the XGB model, the web calculator (https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py) was developed to evaluate the risk of RCC with LM. CONCLUSIONS: This XGB model has the best predictive effect on RCC with LM. The web calculator constructed based on the XGB model has great potential for clinicians to make clinical decisions and improve the prognosis of RCC patients with LM.

背景：肾细胞癌（RCC）是一种高度转移性的泌尿系统癌症。伴肝转移（LM）的 RCC 预后不良。本研究旨在开发一种机器学习（ML）模型，以预测伴 LM 的 RCC 风险，从而辅助临床治疗。

方法：从监测、流行病学和最终结果（SEER）数据库中提取了 42547 例 RCC 患者的回顾性研究数据。ML 包括算法方法，是一个快速发展的领域，已广泛应用于生物医学领域。逻辑回归（LR）、梯度提升机（GBM）、极端梯度提升（XGB）、随机森林（RF）、决策树（DT）和朴素贝叶斯模型[朴素贝叶斯分类器（NBC）]被应用于开发预测模型，以预测伴 LM 的 RCC 风险。这六种模型进行了 10 折交叉验证，并根据曲线下面积（AUC）值选择表现最佳的模型。基于最佳 ML 模型构建了一个在线计算器。

结果：多变量回归分析显示，骨转移、肺转移、分级、T 分期、N 分期和肿瘤大小是伴 LM 的 RCC 发展的独立危险因素。此外，通过热图显示了六种临床变量的相对比例的相关性。在伴 LM 的 RCC 预测模型中，六种 ML 算法中 XGB 模型的平均 AUC 为 0.947。基于 XGB 模型，开发了在线计算器（https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py），以评估伴 LM 的 RCC 风险。

结论：该 XGB 模型对伴 LM 的 RCC 具有最佳预测效果。基于 XGB 模型构建的在线计算器具有很大的潜力，可帮助临床医生做出临床决策，改善伴 LM 的 RCC 患者的预后。