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使用机器学习预测前列腺特异性抗原在不同切断点的前列腺活检结果:一项多中心研究

Mostafa A Arafa^1,2, Karim H Farhat³, Sherin F Aly⁴

¹The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.

Journal of the Egyptian National Cancer Institute

|March 17, 2025

概括

机器学习模型有效地对前列腺活检结果进行分类,区分无癌症,临床显著和非临床显著的癌症. 模型显示前列腺特异性抗原 (PSA) 水平超过10 ng/ml的准确性更高.

科学领域:

人工智能的人工智能
医疗信息学医疗信息学
在瘤学瘤学.

背景情况:

机器学习 (ML) 提高了前列腺活检结果的预测准确性.
区分无癌症,临床显著性和非临床显著性前列腺癌至关重要.

研究的目的:

开发一个新的决策支持ML模型.
在两个前列腺特异抗原 (PSA) 值中对前列腺癌结果进行分类:≤10 ng/ml和>10 ng/ml.

主要方法:

利用了两家沙特阿拉伯医院 (2018年7月至2024年7月) 的数据.
采用了六个ML算法,训练验证分为8:2.
使用准确度,精度,回忆,F1得分和AUC评估性能,并进行外部验证.

主要成果:

随机森林,额外树和决策树算法展示了出色的分类能力.
与PSA≤10 ng/ml (0.92-0.95) 相比,PSA>10 ng/ml (0.96,0.99) 的高性能指标 (精度,AUC) 被观察到.
非临床显著和活检阴性病例比临床显著病例更有效地被分类.

结论:

ML模型对于区分前列腺活检结果是有效的.
这些模型提高了前列腺癌管理中的诊断准确性.
ML有可能改变前列腺癌的临床实践.

关键词:

机器学习预测模型前列腺活检结果前列腺特异性抗原切断线.

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