Department of Urology, Gansu Provincial Hospital, Lanzhou, Gansu, China.
J Cancer Res Clin Oncol. 2023 Sep;149(11):8759-8768. doi: 10.1007/s00432-023-04816-w. Epub 2023 May 2.
This study aims to establish the best prediction model of lymph node metastasis (LNM) in patients with intermediate- and high-risk prostate cancer (PCa) through machine learning (ML), and provide the guideline of accurate clinical diagnosis and precise treatment for clinicals.
A total of 24,470 patients with intermediate- and high-risk PCa were included in this study. Multivariate logistic regression model was used to screen the independent risk factors of LNM. At the same time, six algorithms, namely random forest (RF), naive Bayesian classifier (NBC), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR) and decision tree (DT) are used to establish risk prediction models. Based on the best prediction performance of ML algorithm, a prediction model is established, and the performance of the model is evaluated from three aspects: area under curve (AUC), sensitivity and specificity.
In multivariate logistic regression analysis, T stage, PSA, Gleason score and bone metastasis were independent predictors of LNM in patients with intermediate- and high-risk PCa. By comprehensively comparing the prediction model performance of training set and test set, GBM model has the best prediction performance (F1 score = 0.838, AUROC = 0.804). Finally, we developed a preliminary calculator model that can quickly and accurately calculate the regional LNM in patients with intermediate- and high-risk PCa.
T stage, PSA, Gleason and bone metastasis were independent risk factors for predicting LNM in patients with intermediate- and high-risk PCa. The prediction model established in this study performs well; however, the GBM model is the best one.
本研究旨在通过机器学习（ML）建立中高危前列腺癌（PCa）患者淋巴结转移（LNM）的最佳预测模型，并为临床提供准确的临床诊断和精确治疗的指南。
本研究共纳入 24470 例中高危 PCa 患者。采用多变量逻辑回归模型筛选 LNM 的独立危险因素。同时，采用随机森林（RF）、朴素贝叶斯分类器（NBC）、xgboost（XGB）、梯度提升机（GBM）、逻辑回归（LR）和决策树（DT）等 6 种算法建立风险预测模型。基于 ML 算法的最佳预测性能，建立预测模型，并从 AUC、灵敏度和特异性三个方面评估模型性能。
多变量逻辑回归分析显示，T 分期、PSA、Gleason 评分和骨转移是中高危 PCa 患者 LNM 的独立预测因素。通过综合比较训练集和测试集的预测模型性能，GBM 模型具有最佳的预测性能（F1 评分=0.838，AUROC=0.804）。最后，我们开发了一个初步的计算器模型，可以快速准确地计算中高危 PCa 患者的局部 LNM。
T 分期、PSA、Gleason 和骨转移是预测中高危 PCa 患者 LNM 的独立危险因素。本研究建立的预测模型性能良好；然而，GBM 模型是最佳模型。
