Dual-mode ultrasound radiomics and intrinsic imaging phenotypes for diagnosis of lymph node lesions

Ying Chen; Jianwei Jiang; Jie Shi; Wanying Chang; Jun Shi; Man Chen; Qi Zhang

doi:10.21037/atm-19-4630

Dual-mode ultrasound radiomics and intrinsic imaging phenotypes for diagnosis of lymph node lesions

Ann Transl Med. 2020 Jun;8(12):742. doi: 10.21037/atm-19-4630.

Authors

Ying Chen^{1

2

3}, Jianwei Jiang⁴, Jie Shi^{1

2

3}, Wanying Chang⁴, Jun Shi^{1

3}, Man Chen⁴, Qi Zhang^{1

2

3

5}

Affiliations

¹ Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
² The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Institute of Biomedical Engineering, Shanghai University, Shanghai, China.
³ School of Communication and Information Engineering, Shanghai University, Shanghai, China.
⁴ Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁵ Hangzhou YITU Healthcare Technology, Hangzhou, China.

Abstract

Background: The ultrasonic diagnosis of lymph node lesions is usually based on a small number of subjective visual features from a single ultrasonic modality, which limits diagnostic accuracy. Therefore, our study aimed to propose a computerized method for using dual-mode ultrasound radiomics and the intrinsic imaging phenotypes for accurately differentiating benign, lymphomatous, and metastatic lymph nodes.

Methods: A total of 543 lymph nodes from 538 patients were examined with both B-mode ultrasonography and elastography. The data set was randomly divided into a training set of 407 nodes and a validation set of 136 nodes. First, we extracted 430 radiomic features from dual-mode images. Then, we combined the least absolute shrinkage and selection operator with the analysis of variance to select several typical features. We retrieved the intrinsic imaging phenotypes by using a hierarchical clustering of all radiomics features, and we integrated the phenotypes with the selected features for the classification of benign, lymphomatous, and metastatic nodes.

Results: The areas under the receiver operating characteristic curves (AUCs) on the validation set were 0.960 for benign vs. lymphomatous, 0.716 for benign vs. metastatic, 0.933 for lymphomatous vs. metastatic, and 0.856 for benign vs. malignant.

Conclusions: The radiomics features and intrinsic imaging phenotypes derived from the dual-mode ultrasound can capture the distinctions between benign, lymphomatous, and metastatic nodes and are valuable in node differentiation.

Keywords: Lymph nodes; dual mode; intrinsic imaging phenotypes; radiomics; ultrasound.