全球放射性特征从乳房扫描来预测难以解释的正常病例
Somphone Siviengphanom1, Ziba Gandomkar2, Sarah J Lewis2
1Medical Image Optimisation and Perception Group, Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, the University of Sydney, Sydney, NSW, 2006, Australia. ssiv6387@uni.sydney.edu.au.
Journal of digital imaging
|May 30, 2023
概括
乳房影像的全球放射性特征可以预测难以解释的正常病例. 这种放射性分析有助于识别具有挑战性的乳房造影,提高了乳房成像诊断的准确性.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- Mammography 解释涉及主观困难,特别是在正常情况下.
- 识别难以解释的正常病例对于提高阅读器性能和减少诊断错误至关重要.
研究的目的:
- 研究从乳房图片中提取的全球放射性特征 (GRF) 的预测能力,以识别难以解释的正常病例 (NC).
主要方法:
- 从239个正常的乳房影像中提取了34个手工制作的GRF,使用基于格子和平方的方法.
- 开发了三种随机森林分类器 (CC,MLO和CC+MLO) 以区分难以解释的NC和易于解释的NC.
- 通过使用leave-one-out交叉验证验证模型性能,并使用AUC进行评估,并通过DeLong测试进行统计比较.
主要成果:
- 结合CC+MLO模型的性能比单独的CC和MLO模型 (0.66 AUC) 高 (0.71 AUC),但在统计学上并不显著.
- 六个GRF被确定为难以解释的NC的有价值预测指标.
- 20个放射性特征在难以解释和易于解释的NC之间存在显著差异 (p < 0.05).
结论:
- 从乳房影像中提取的全球放射性特征表明,在预测难以解释的正常病例方面具有潜力.
- 放射性分析提供了一种定量方法来评估乳房影像的解释性,这可能有助于质量控制和读者培训.
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