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在骨和软组织瘤中整合放射学,人工智能和分子特征:诊断和预后方面的进步

Guochao Li¹, Peipei Feng¹, Yayun Lin²

¹Department of Imaging, Yantaishan Hospital, Yantai, Shangdong, China.

Frontiers in oncology

|September 3, 2025

概括

放射学和人工智能在诊断骨和软组织瘤方面表现有前途. 然而,整合分子数据仍然是一个重大挑战,阻碍了全面的诊断工具的开发.

科学领域:

癌症学
医学成像
生物信息学

背景情况:

骨和软组织瘤带来了诊断和预后方面的挑战.
像放射学和人工智能 (AI) 这样的先进计算技术提供了潜在的解决方案.
整合不同的数据模式对于改善临床结果至关重要.

研究的目的:

系统地审查放射学,人工智能和分子签名的整合,以诊断和预测BSTT.
确定该领域的当前应用,方法和局限性.
概述多模式诊断系统的未来研究方向.

主要方法:

按照PRISMA 2020指南进行系统审查.
在主要的科学数据库中进行全面搜索 (PubMed,Scopus,Web of Science,Google Scholar).
对24项专注于放射学,人工智能和BST分子数据的研究进行分析.

主要成果:

建立了放射学-人工智能管道,主要使用具有机器学习分类器的MRI和CT.
分子数据的整合是最小的,很少有研究结合了他的病理相关性.
主要应用包括瘤分类,化疗反应预测和转移检测,在验证模型中显示高AUC.
在标准化的多原子融合,外部验证和深度学习模型的可解释性方面存在重大差距.

结论:

目前的研究主要集中在BSTT的放射学-AI上,在整合分子数据方面取得了有限的成功.
迫切需要标准化方法,外部验证和可解释的人工智能来弥合成像与分子之间的差距.
开发多模式诊断系统需要共识共享数据和先进的建模技术.

关键词:

人工智能骨和软组织瘤分子特征放射基因组学辐射学

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