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使用机器学习预测前列腺癌风险的系统,有针对性和组合活检的比较:一项多中心研究
Mostafa A Arafa1,2, Islam Omar3, Karim H Farhat1
1The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
概括
机器学习模型可以准确预测前列腺癌 (PCa) 风险. 针对性和组合活检检测方法显示出比单独的系统活检更高的性能,用于PCa诊断.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 前列腺癌 (PCa) 诊断依赖于各种临床因素和活检技术.
- 准确的预后预测模型对于及时有效的PCa管理至关重要.
- 在不同活检策略中比较机器学习 (ML) 模型性能对于优化检测至关重要.
研究的目的:
- 开发新的机器学习 (ML) 模型来预测前列腺癌 (PCa) 风险.
- 评估和比较这些ML模型的有效性,使用系统与有针对性的活检检测技术.
主要方法:
- 在沙特阿拉伯利雅得,2019-2023年期间诊断出PCa的528名患者数据的分析.
- 利用四个机器学习 (ML) 算法,包括随机森林 (RF) 和XGBoost (XGB),用于PCa预测和分类.
- 基于诸如前列腺特异性抗原 (PSA),MRI发现和活检类型等因素评估模型性能.
主要成果:
- 年龄,前列腺体积,PSA,BMI,mpMRI得分和病变特征与PCa有显著关联.
- 随机森林 (RF) 和XGBoost (XGB) 模型在预测PCa方面表现出很高的准确性.
- 与单独的系统活检相比,目标活检和组合活检的模型获得了优异的性能 (AUC 0.94-0.97).
结论:
- 随机森林 (RF) 模型对PCa风险具有出色的预测能力,特别是在有针对性和组合活检方法中.
- 机器学习 (ML) 模型显示出作为查工具的前景,有可能减少遗漏的PCa诊断.
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