文章：

机器学习模型在预测日本局限性前列腺癌淋巴结转移中的应用价值

Utility of Machine Learning Models to Predict Lymph Node Metastasis of Japanese Localized Prostate Cancer

原文发布日期：5 December 2024

DOI: 10.3390/cancers16234073

类型: Article

开放获取: 是

英文摘要：

Background/Objectives: Extended pelvic lymph node dissection is a crucial surgical technique for managing intermediate to high-risk prostate cancer. Accurately predicting lymph node metastasis before surgery can minimize unnecessary lymph node dissections and their associated complications. This study assessed the efficacy of various machine learning models for predicting lymph node metastasis in a cohort of Japanese patients who underwent robot-assisted laparoscopic radical prostatectomy. Methods: Data from 625 patients who underwent extended pelvic lymph node dissection or standard dissection with lymph node metastasis between October 2010 and February 2023 were analyzed. Four machine learning models—Random Forest, Light Gradient-Boosting Machine, Logistic Regression, and Support Vector Machine—were used to predict lymph node metastasis. Their performance was assessed using receiver operating characteristic curves, a decision curve analysis, and predictive values at different thresholds. Results: Lymph node metastasis was observed in 34 patients (5.4%). The Light Gradient-Boosting Machine had the highest AUC of 0.924, followed by the Random Forest model with an AUC of 0.894. The decision curve analysis indicated substantial net benefits for both models, particularly at low threshold probabilities. The Light Gradient-Boosting Machine demonstrated superior accuracy, achieving 95.6% at the 0.05 threshold and 96.7% at the 0.10 threshold, outperforming other models and conventional nomograms in the validation dataset. Conclusion: Machine learning models, especially Light Gradient-Boosting Machine and Random Forest, show significant potential for predicting lymph node metastasis in prostate cancer, thereby aiding in reducing unnecessary surgical interventions.

摘要翻译： 

背景/目的：扩大盆腔淋巴结清扫术是处理中高危前列腺癌的关键外科技术。术前准确预测淋巴结转移可最大限度减少不必要的淋巴结清扫及其相关并发症。本研究评估了多种机器学习模型在预测接受机器人辅助腹腔镜根治性前列腺切除术的日本患者队列中淋巴结转移的效能。方法：分析了2010年10月至2023年2月期间接受扩大盆腔淋巴结清扫或标准清扫且存在淋巴结转移的625例患者数据。采用随机森林、轻量梯度提升机、逻辑回归和支持向量机四种机器学习模型预测淋巴结转移，并通过受试者工作特征曲线、决策曲线分析及不同阈值下的预测值评估其性能。结果：34例患者（5.4%）观察到淋巴结转移。轻量梯度提升机模型的曲线下面积最高（0.924），随机森林模型次之（0.894）。决策曲线分析显示两种模型均具有显著净获益，尤其在低阈值概率区间。轻量梯度提升机在验证数据集中展现出最优预测精度：0.05阈值下准确率达95.6%，0.10阈值下达96.7%，其性能优于其他模型及传统列线图。结论：机器学习模型（特别是轻量梯度提升机与随机森林）在前列腺癌淋巴结转移预测方面具有显著潜力，有助于减少不必要的外科干预。

原文链接：

Utility of Machine Learning Models to Predict Lymph Node Metastasis of Japanese Localized Prostate Cancer