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基于机器学习的放射学用于从大脑的磁共振图像中对质瘤等级进行分类

Anuj Kumar¹, Ashish Kumar Jha², Jai Prakash Agarwal¹

¹Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai 400012, India.

Journal of personalized medicine

|June 28, 2023

概括

放射学和机器学习可以从MRI扫描中准确预测质瘤等级,为主观放射学评估提供一种非侵入性的替代方案. 这种方法有助于确定脑瘤患者的预后和生存率.

科学领域:

放射学放射学是一门学科.
在瘤学瘤学.
数据科学数据科学数据科学

背景情况:

质瘤分级对于预后和生存至关重要.
当前的语义放射学分级是主观的,容易出现错误.
需要一种非侵入性,客观的方法来分类质瘤.

研究的目的:

开发和验证基于放射学的机器学习模型,用于手术前质瘤分级.
为了比较不同机器学习分类器在分类质瘤等级中的性能.

主要方法:

83名患有质瘤的患者接受了MRI.
使用TexRad软件从T2WMRI序列中提取了放射学特征.
使用了递归特征消除和五种机器学习分类器 (SVM,随机森林,梯度提升,天真贝叶斯,AdaBoost).
用10倍交叉验证进行模型评估.

主要成果:

随机森林模型实现了最高的性能.
随机森林模型的关键指标包括AUC为0.81,准确度为0.83,F1得分为0.88,回忆力为0.93,精度为0.85.
来自单个T2W序列的放射特征足以进行强大的分类.

结论:

基于机器学习的放射学功能提供了一种非侵入性方法,用于术前质瘤等级的预测.
这种方法可以提高诊断准确度,并有助于临床决策.
在更大的数据集上进行进一步验证是有必要的.

关键词:

这些分类器是分类器的分类器.质瘤等级的质瘤等级.机器学习是机器学习.无线电学 (radiomics) 是一种无线电学.

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