Dictionary learning LASSO for feature selection with application to hepatocellular carcinoma grading using contrast enhanced magnetic resonance imaging

Frontiers in Oncology

Oncology

Bao-Lin Ye¹  Lei Lei¹  Jian-Peng Yuan²  Cong Wang³  ZuJun Hou³  Li-Xin Du⁴  Pan Wang⁴  Ying-Long He⁵ 

[1] College of Information Science and Engineering, Jiaxing University, Jiaxing, China;Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China;Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China;Medical Imaging Department, Shenzhen Longhua District Central Hospital, Shenzhen, China;School of Mechanical Engineering Sciences, University of Surrey, Guildford, United Kingdom;

关键词: hepatocellular carcinoma (HCC);    radiomics;    feature selection;    magnetic resonance imaging (MRI);    least absolute shrinkage and selection operator (LASSO) dictionary learning;   

DOI  :  10.3389/fonc.2023.1123493

 received in 2022-12-14, accepted in 2023-03-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionThe successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature.MethodsThis study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary.Results and discussionThe experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.

【 授权许可】

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Copyright © 2023 Lei, Du, He, Yuan, Wang, Ye, Wang and Hou