机器学习驱动的放射学在18F-FDGPET上用于质瘤诊断:系统性审查和元分析
Ali Shahriari1, Sasan Ghazanafar Ahari2, Ali Mousavi2
1Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
使用18F-FDG PET放射学的机器学习模型显示了高精度的质瘤分类. 需要进一步标准化和验证临床使用.
科学领域:
- 神经瘤学
- 医学成像
- 机器学习
- 辐射学
背景情况:
- 机器学习 (ML) 应用在放射学上已经推进了神经瘤成像.
- 使用18F-FDG PET放射性特征进行质瘤分类的ML模型的诊断实用性尚未得到充分证实.
研究的目的:
- 系统地评估和定量合成经过18F-FDG PET放射学训练的ML模型的诊断准确性.
主要方法:
- 使用ML对18F-FDGPET放射性特征进行符合PRISMA标准的系统审查和分析.
- 搜索了PubMed,Scopus和科学网络,直到2025年1月.
- 包括报告绩效指标的研究;提取人口统计,成像,特征,模型和验证的数据;执行随机效应的元分析.
主要成果:
- 分析了12项涉及2321名患者的研究.
- 综合的诊断指标包括准确率为92. 6%,AUC为0. 95,灵敏度为85. 4%,特异性为89. 7%,F1分数为0. 78和精度为0. 90.
- 观察到高异质性;CNN和PET/MRI整合显示出优异的性能.
结论:
- 基于18F-FDG PET放射学的ML模型对质瘤分类具有强大的诊断性能.
- 在临床整合之前,需要标准化管道,外部验证和透明报告.
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