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预测重复前列腺活检的积极结果:机器学习方法的比较,以确定关键参数和最佳算法

Xinru Zhang¹, Chao Feng¹, Xiao Bai²

¹Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233 Shanghai, China.

Archivos espanoles de urologia

|October 23, 2023

概括

这项研究开发了一种机器学习模型,用于预测阳性重复前列腺活检,识别前列腺体积和PSA水平等关键因素,以提高诊断准确度和减少不必要的程序.

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.
医疗信息学医疗信息学

背景情况:

需要创新的策略来改善前列腺癌的诊断和减少重复活检.
这项研究确定了影响重复前列腺活检结果的关键参数.

研究的目的:

确定影响重复前列腺活检结果的显著参数.
开发一种最佳的机器学习算法,用于预测阳性重复前列腺活检结果.

主要方法:

分析了174名接受重复前列腺活检的男性的数据.
采用了六种特征选择方法,并评估了七种机器学习算法,包括支持矢量分类 (SVC).

主要成果:

支持矢量分类 (SVC) 显示出卓越的准确性 (0.7365) 和高ROC AUC (0.6871).
阳性重复活检的关键预测因素包括初始/最新的前列腺体积,PSA水平,fPSA/PSA比率和年龄.

结论:

开发了一个基于SVC的机器学习算法,用于预测阳性重复前列腺活检结果.
初始和最新的前列腺体积,PSA水平,fPSA/PSA比率和年龄是预测模型中的重要因素.

关键词:

活组织活检癌症癌症癌症癌症癌症机器学习是机器学习.诺莫格拉姆 (Nomograms) 是一个名字.前列腺前列腺前列腺

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