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基于MRI-Radiomics的机器学习方法在脑瘤分类中的质量评估:系统审查

Shailesh S Nayak¹, Saikiran Pendem¹, Girish R Menon²

¹Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

Diagnostics (Basel, Switzerland)

|December 17, 2024

概括

对脑瘤的放射学分析显示,在分类质瘤方面具有很高的准确性,优于传统方法. 临床整合需要进一步验证,以改善患者的治疗结果.

科学领域:

神经瘤学神经瘤学
医学成像分析医学成像分析
机器学习在医学中的应用

背景情况:

大脑瘤在瘤学中构成诊断挑战.
放射学提取定量成像特征进行分析.
放射性研究的方法质量需要评估.

研究的目的:

系统地审查对脑瘤的放射性研究.
使用放射学质量评分 (RQS) 评估放射学方法的质量.
评估放射学在质瘤分类方面的潜力.

主要方法:

在PubMed的系统文献搜索中,搜索有关脑瘤的放射学研究 (2015年以后).
包括/排除标准适用于300个已识别的文章,其中18个被选择用于合成.
用于训练和验证机器学习模型用于质瘤分类的放射性特征.

主要成果:

18项研究符合纳入标准,证明了放射学在质瘤分类中的潜力.
使用了各种成像方式 (MRI,PET/CT,ASL,DTI).
机器学习算法 (深度学习,SVM,随机森林) 实现了高分类准确度.

结论:

基于放射学的机器学习在质瘤分类中显示出高准确度,超过了传统方法.
进一步的验证和标准化对于临床整合至关重要.
放射学领域的开放科学实践提高了透明度和协作.

关键词:

质瘤是一种质瘤.差异诊断是一种差异诊断.无线电学 (radiomics) 是一种无线电学.无线电学质量评分无线电学质量评分质地分析,质地分析.

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