基于机器学习算法的风险预测和查检测的前列腺癌在良性前列腺增生队列中的前列腺癌
Chia-Cheng Chang1, Jiun-Kai Chiou1, Cheng-Jian Lin2
1Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C.
Anticancer research
|March 27, 2024
概括
机器学习模型可以预测良性前列腺增生 (BPH) 患者的前列腺癌 (PCa) 风险. 体重指数和前列腺特异性抗原水平是风险评估的关键指标.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 在瘤学瘤学.
- 数据科学数据科学数据科学
背景情况:
- 前列腺癌 (PCa) 构成了严重的致命威胁.
- 良性前列腺增生 (BPH) 影响了大量患者群体.
- 在BPH患者中准确的PCa风险预测对于及时干预至关重要.
研究的目的:
- 使用机器学习预测BPH患者的PCa风险.
- 确定这一群体中PCa发展的关键风险因素.
- 优化预测模型性能,以提高临床效用.
主要方法:
- 使用临床数据库 (2000-2020) 的回顾性队列研究.
- 包括服用特定药物的BPH患者,但不包括先前有癌症诊断的患者.
- 机器学习算法的应用:极端梯度提升 (XGB),支持矢量机 (SVM) 和K-最近邻居 (KNN).
主要成果:
- 支持向量机 (SVM) 和极端梯度提升 (XGB) 模型显示,与K-最近邻居 (KNN) 相比,曲线下的精度和面积更高.
- 发现的关键预测因素包括身体质量指数 (BMI),晚期前列腺特异性抗原 (PSA) 和PSA速度.
- 使用5α-减少酶抑制剂与PCa发病率的增加有关,尽管生存结果相似.
结论:
- 机器学习为BPH患者个性化PCa风险评估提供了一个有希望的途径.
- 需要进一步的研究来完善模型和减轻数据偏差.
- 临床医生应将这些ML工具视为常规查方法的辅助工具.
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