新型CovBat协调方法对增强放射学特征稳定性和机器学习模型性能的影响:一个多中心,多设备的研究
Chuanghui Zhou1, Jianwei Zhou2, Yijun Lv3
1Department of Imaging Diagnosis, Nanfang Hospital, Southern Medical University, Guangzhou 510000, Guangdong, China; School of Medical and Information Engineering, Gannan Medical University, Ganzhou 341000, Jiangxi, China.
European journal of radiology
|February 5, 2025
概括
新的CovBat协调方法显著降低了来自各种CT扫描仪的放射学特征变异性,提高了在多中心研究中的机器学习模型性能.
科学领域:
- 医学成像分析 医学成像分析
- 无线电学 (Radiomics) 是一种无线电学.
- 机器学习 机器学习
背景情况:
- 多中心研究面临的挑战是,由于不同的成像设备,放射学特征的变化.
- 协调技术对于标准化放射学数据至关重要.
- 将新的协调方法与现有方法进行比较对于提高数据一致性至关重要.
研究的目的:
- 评估CovBat协调方法在减少放射性特征变异方面的有效性.
- 将CovBat的性能与多中心CT数据的ComBat方法进行比较.
- 评估CovBat对机器学习模型性能的影响.
主要方法:
- 来自三个机构的1000张腹部CT扫描的回顾性分析,使用各种扫描仪.
- 使用PyRadiomics从肝脏和脏中提取93个放射性特征.
- 在数据协调方面应用非协调,ComBat和CovBat方法.
- 构建和评估用于跨不同特征类的二进制分类的机器学习模型.
主要成果:
- 科夫巴特增加了73.12%的连续性放射学特征,超过了康巴特的68.82%.
- CovBat将硬件相关的特征变化降低到1.19-1.88%,与ComBat.Bat的1.89-2.01%相比.
- 机器学习模型的AUC与CovBat显著改善,在组合模型中达到1.00.
结论:
- CovBat有效地减少了来自CT扫描仪差异的放射学特征变化.
- 在提高机器学习模型准确度方面,CovBat方法表现出卓越的性能.
- 虽然CovBat提供了显著的改进,但增强的程度在射电学特征类别之间有所不同.
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