Sarcoidosis 的诊断方面的挑战
Karol Bączek1, Wojciech J Piotrowski1, Francesco Bonella2
1Department of Pneumology, Medical University of Łódź, Poland.
Current opinion in immunology
|September 3, 2025
概括
诊断肉类瘤是复杂的,通常需要组合的方法. 新兴的
科学领域:
- 肺病学和免疫学
- 诊断医学
- 生物标志物发现
背景情况:
- 由于不同的临床表现和模仿其他疾病的组织学发现, Sarcoidosis 诊断具有挑战性.
- 目前的诊断方法包括组织学,成像学和生物标志物,但没有任何单一的测试是决定性的.
研究的目的:
- 审查目前关于肉类瘤诊断的证据,包括组织病理学,采样技术,成像和生物标志物.
- 探索新兴"omics"和人工智能 (AI) 工具的潜力,以提高诊断准确性.
主要方法:
- 目前关于肉病诊断方式的文献综述.
- 对新兴技术的分析,如内支气管超声波导向的跨支气管针吸收 (EBUS-TBNA),冷活检,PET/MRI和多组学.
- 评估人工智能和深度学习算法与成像和omics数据的结合.
主要成果:
- 在粒状病变中,有限的亡是 Sarcoidosis 活检中的辅助发现.
- 采用EBUS-TBNA是首选的采样方法;冷活检需要进一步验证.
- 皮肤图像测量和皮肤图像/MRI 增强了疾病活动的评估和心脏参与的检测.
- 虽然先进的BAL免疫类型和其他生物标志物与炎症相关,但它们的诊断价值有限.
- 在深度学习的帮助下,多组学签名和放射学显示出对非侵入性诊断,表型和监测的希望.
结论:
- 一个综合的,多学科的方法对于准确的类病诊断至关重要.
- 没有单一的诊断测试是足够的.
- 需要大型,多中心,多民族的研究来验证人工智能和奥米克研究的临床应用.
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