基于CT的放射性成像生物标志物在本源脏活检中的组织病理相关性
Yoon Ho Choi1, Ji-Eun Kim2, Ro Woon Lee3
1Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
BMC medical imaging
|September 28, 2024
概括
CT扫描放射性特征与慢性病组织学变化有显著的相关性. 放射学模型可以非侵入性地预测纤维化,从而有可能改善慢性病的评估.
科学领域:
- 放射学 放射学是一门学科.
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能在医学中的应用
背景情况:
- 脏活检是诊断脏疾病的标准.
- 慢性病 (CKD) 的进展与间歇性纤维化和管管缩有关.
- 准确的预后信息对于管理CKD至关重要.
研究的目的:
- 评估基于CT的放射性特征与本地脏活检中的慢性组织学变化之间的相关性.
- 开发和验证放射学预测模型,用于评估脏慢性病等级.
主要方法:
- 包括年龄≥18岁的患者,在一周内接受脏活检和CT扫描.
- 深度学习细分脏;放射性特征 (形状,纹理) 被提取.
- 机器学习模型被开发用于预测中度至重度的慢性病.
主要成果:
- 在放射性特征和慢性脏组织学等级之间发现了显著的相关性.
- 纹理特征和总细胞特征显示出比皮质ROI特征更强的相关性.
- 放射学模型的AUC为0.89对于帕伦基马特征和0.74对于皮质ROI特征.
结论:
- 基于CT的放射性特征与慢性脏组织学变化有显著的相关性.
- 放射学预测模型显示了对纤维化的非侵入性评估的潜力.
- 这种方法可以提高慢性病的评估.
相关概念视频
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