Benchmarking Feature Selection Methods in Radiomics
Benchmarking Feature Selection Methods in Radiomics
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DOI：
10.1097/rli.0000000000000855
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发表时间：
2022-07-01
影响因子：
6.7
通讯作者：
Demircioglu, Aydin
中科院分区：
文献类型：
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作者：
Demircioglu, Aydin
目的放射学研究中的一个关键问题是数据集的高维，这是由于样本量小和从感兴趣的体积中提取许多通用特征造成的。因此，使用了特征选择方法，其目的是去除冗余和不相关的特征。由于有许多特征选择算法，因此在放射组学的背景下理解它们的性能是关键。材料与方法在10个公开可用的放射学数据集上对29种特征选择算法和10种分类器进行评估。比较了特征选择方法的训练次数、所选特征的稳定性以及衡量方法成对相似性的排序。此外，利用性能最好的分类器的接收器操作特征曲线下的面积来衡量算法的预测性能。结果特征选择在训练时间、稳定性和相似性方面有很大差异。没有一种方法能够在预测性能上持续优于另一种方法。结论我们的结果表明，简单的方法比复杂的方法更稳定，在接收器工作特性曲线下的面积方面没有更差的表现。方差分析、最小绝对收缩和选择算子以及最小冗余度、最大相关性集成在预测性能方面似乎是放射学研究的良好选择，因为它们的表现优于大多数其他特征选择方法。
Objectives A critical problem in radiomic studies is the high dimensionality of the datasets, which stems from small sample sizes and many generic features extracted from the volume of interest. Therefore, feature selection methods are used, which aim to remove redundant as well as irrelevant features. Because there are many feature selection algorithms, it is key to understand their performance in the context of radiomics. Materials and Methods A total of 29 feature selection algorithms and 10 classifiers were evaluated on 10 publicly available radiomic datasets. Feature selection methods were compared for training times, for the stability of the selected features, and for ranking, which measures the pairwise similarity of the methods. In addition, the predictive performance of the algorithms was measured by utilizing the area under the receiver operating characteristic curve of the best-performing classifier. Results Feature selections differed largely in training times as well as stability and similarity. No single method was able to outperform another one consistently in predictive performance. Conclusion Our results indicated that simpler methods are more stable than complex ones and do not perform worse in terms of area under the receiver operating characteristic curve. Analysis of variance, least absolute shrinkage and selection operator, and minimum redundancy, maximum relevance ensemble appear to be good choices for radiomic studies in terms of predictive performance, as they outperformed most other feature selection methods.