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放射学和机器学习在脑瘤及其息地:一个系统的审查

Mehnaz Tabassum^1,2, Abdulla Al Suman², Eric Suero Molina^2,3

¹Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia.

|August 12, 2023

概括

放射学和机器学习通过提取定量成像特征来提供先进的脑瘤分析. 这种方法增强了诊断,预后和对瘤息地相互作用的理解.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

放射学从医学图像 (CT,MRI) 中提取定量特征,用于客观的瘤分析.
它有助于脑瘤诊断,预后和理解超出视觉检查的瘤特征.
瘤及其微环境 (息地) 对彼此的行为产生相互影响.

研究的目的:

系统地审查目前的局限性和放射学和机器学习在分析脑瘤及其息地方面的未来发展.
探索放射学和机器学习的整合,以改善脑瘤诊断和患者结果预测.
调查放射学在评估瘤息地相互作用中的作用.

主要方法:

在脑瘤分析中对放射学和机器学习现有文献进行系统审查.
分析放射学数据与瘤遗传学,等级,复发和治疗反应的相关性.
探索机器学习应用到高维放射学数据的探索.

主要成果:

放射学提供了详细的,客观的瘤特征,超越了视觉评估.
机器学习增强了放射学数据的诊断和预后能力.
了解瘤息地相互作用对于全面分析至关重要.

结论:

放射学和机器学习在推进脑瘤诊断和治疗方面具有重大潜力.
需要进一步的研究来克服局限性,并充分利用这些技术.
整合放射学,机器学习和息地分析有望改善患者的治疗结果.

关键词:

大脑瘤是个大脑瘤机器学习是机器学习.神经瘤学神经瘤学周围口区域周围口区域无线电学 (radiomics) 是一种无线电学.瘤息地瘤息地

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