从可变辐射剂量图像中对肺瘤和死亡风险的不确定性意识定量CT评估
David Baraghoshi1,2, Matthew J Strand3,4, Stephen M Humphries5
1Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA. baraghoshid@njhealth.org.
European radiology
|April 4, 2025
概括
这项研究开发了一种不确定性意识的深度学习模型,用于使用CT扫描进行一致的肺气和死亡风险评估. 这种新方法提高了不同成像协议的稳定性,提高了COPD评估的临床实用性.
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
- 肺部医学 肺部医学
背景情况:
- 脑瘤的定量CT评估对CT协议敏感,导致不确定性和临床效用降低.
- 肺瘤评估的传统指标可能会受到成像参数变化的影响.
- 需要自动化,一致的方法来评估肺气和死亡风险.
研究的目的:
- 开发一种自动化方法,共同和一致地评估肺气和死亡风险.
- 在基于CT的肺瘤评估中量化数据和模型不确定性.
- 为了提高不同CT辐射剂量协议的肺瘤量化的稳定性.
主要方法:
- 在COPDGene研究中的胸部CT扫描上训练了一种多任务贝叶斯神经网络 (BNN).
- 不管采购协议如何,BNN估计了经体积调整的肺密度 (ALD) 和5年死亡风险.
- 将深度学习ALD (DL-ALD) 与传统ALD进行比较,并量化数据和模型不确定性.
主要成果:
- 与传统ALD相比,DL-ALD在全剂量 (FD) 和减少剂量 (RD) 的CT图像之间显示出更高的一致性.
- 预测的5年死亡风险显示图像协议之间的差异很小.
- 在患有严重肺气的参与者中,不确定性量化较低,在严重和早期阶段的参与者中,死亡风险较低.
结论:
- 多任务BNN为CT评估肺瘤的成像协议提供了更好的稳定性.
- 该方法提供直接测量不确定性,增强跨多种成像协议和患者群体的概括性.
- 意识到不确定性的深度学习提高了COPD评估的肺瘤量化和死亡风险评估的一致性.
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