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放射性分析和不同机器学习模型在前列腺癌诊断中的作用
Eleni Bekou1, Ioannis Seimenis2, Athanasios Tsochatzis3
1Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
Journal of imaging
|August 27, 2025
概括
使用双参数MRI放射学的机器学习模型显示,当包括前列腺特异性抗原 (PSA) 指数时,前列腺癌分级得到改善. 这可以提高诊断准确度,从而有效地规划治疗.
科学领域:
- 医学成像
- 癌症学
- 人工智能
背景情况:
- 前列腺癌 (PCa) 是男性最常见的癌症,需要精确分类才能有效治疗.
- 双参数磁共振成像 (bpMRI) 放射学与机器学习 (ML) 结合显示了增强PCa诊断和预后的潜力.
研究的目的:
- 评估七种ML模型在区分PCa等级的诊断效率.
- 评估输入变量的影响,包括T2加权 (T2W) 和扩散加权 (DWI) 的MRI和前列腺特异性抗原 (PSA) 值对模型性能的影响.
主要方法:
- 总共包括214名接受bpMRI治疗的男性.
- 从T2W和DWI序列中提取了放射性特征.
- 使用这些特征训练和测试了七个ML算法,包括PSA指数和不包括PSA指数,并通过接收器运行特征曲线分析进行评估.
主要成果:
- 仅使用T2W和DWI放射性特征的ML模型显示临床效用有限 (AUC为0. 7030. 807).
- 整合PSA指数显著改善了所有等级和位置的模型性能 (AUC为0.7841.00).
- 模型的性能高度依赖于所使用的输入参数.
结论:
- 与bpMRI放射学相结合的ML可以帮助解决前列腺癌的差异诊断问题.
- 包含PSA指数对于优化PCa分级中的ML模型效率至关重要.
- 进一步优化分析方法对于临床应用至关重要.
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