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机器学习预测磁共振成像前列腺成像报告和数据系统与目标前列腺活检结果相关吗?

Mostafa A Arafa^1,2, Karim H Farhat¹, Nesma Lotfy³

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

Medical principles and practice : international journal of the Kuwait University, Health Science Centre

|May 26, 2025

概括

机器学习模型可以准确地预测前列腺成像报告和数据系统 (PI-RADS) 从MRI扫描中的得分. 这些模型改善了前列腺癌的检测,通过识别遗漏病例,可能减少不必要的活检.

科学领域:

放射学和医学成像学医学成像学
医疗保健中的人工智能
在瘤学瘤学.

背景情况:

前列腺癌的诊断依赖于磁共振成像 (MRI) 和前列腺成像报告和数据系统 (PI-RADS) 的评分.
准确的PI-RADS评分对于指导前列腺活检决策和检测临床显著前列腺癌 (csPCa) 至关重要.
机器学习 (ML) 提供了提高医学成像诊断准确性的潜力.

研究的目的:

使用各种ML算法预测和分类MRIPI-RADS得分.
评估ML预测PI-RADS得分与前列腺活检结果之间的一致性.
评估ML在改善csPCa检测和减少不必要的活检方面的潜力.

主要方法:

开发ML模型,包括随机森林和额外树木,用于PI-RADS得分预测和分类.
使用精度和曲线下的面积 (AUC) 评估模型性能.
确定影响结果分类的关键特征,如PSA水平,PSA密度和病变直径.

主要成果:

随机森林和额外树木模型实现了高精度 (91.95%) 和AUC (0.9329和0.9404分别).
确定PSA水平,PSA密度和病变直径是最重要的预测特征.
ML预测增强了PI-RADS分类,在低风险PI-RAD类中确定了1.9%之前错过的csPCa病例.

结论:

预测性ML模型在预测MRIPI-RADS得分和区分风险类别方面表现出卓越的能力.
ML集成可以通过提高低风险患者的csPCa检测和完善高风险分类来提高PI-RADS实用性.
将ML驱动的PI-RADS得分与PSA密度和病变特征等临床参数相结合,可以帮助医生在诊断前列腺癌时做出决策.

关键词:

活检结果的结果.机器学习预测的预测.前列腺成像报告和数据系统前列腺癌是什么意思前列腺癌是什么意思

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