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Shear wave elastography-based ultrasomics: differentiating malignant from benign focal liver lesions

Abdominal Radiology volume 46, pages 237–248 (2021)Cite this article

Abstract

Purpose

Ultrasomics is a radiomics technique that extracts high-throughput quantitative data from ultrasound imaging. The aim of this study was to differentiate malignant from benign focal liver lesions (FLLs) using two-dimensional shear wave elastography (2D-SWE)-based ultrasomics.

Methods

A total of 175 FLLs in 169 patients were prospectively analyzed. The study population was divided into a training cohort (n = 122) and a validation cohort (n = 53). The maxima, minima, mean, and standard deviation of 2D-SWE measurements were expressed in kilopascals (Emax, Emin, Emean, and ESD). The ultrasonics technique was used to extract the features from the 2D-SWE images. Support vector machine was used to establish two prediction models: the ultrasomics score (ultrasomics features only) and the combined score (SWE measurements and ultrasomics features). The diagnostic performance of the models in differentiating FLLs was analyzed.

Results

A total of 1044 features were extracted and 15 features were selected. The AUC for the combined score, ultrasomics score, Emax, Emean, Emin and ESD were 0.94, 0.91, 0.92, 0.89, 0.67, and 0.89, respectively. The combined score had the best diagnostic performance. The sensitivity, specificity, PPV, NPV, +LR, LR of the combined score were 92.59%, 87.50%, 94.59%, 82.50%, 7.35%, and 0.09%, respectively. The decision curve analysis results showed that when the threshold probability was > 29%, the combined score showed improved benefits for patients compared to using the ultrasomics score and 2D-SWE measurements.

Conclusion

The results of this study demonstrated that the combined score had good diagnostic accuracy in differentiating malignant from benign FLLs.

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Funding

This study was funded by the National Nature Science Foundation of China (NO. 81601500), Guangdong Nature Science Foundation (NO.2017A030313570), Guangzhou Science and Technology Foundation (NO. 201607010050, NO. 201904010187).

Author information

Author notes

  1. Wei Wang and Jian-Chao Zhang have contributed equally to this work.

Affiliations

  1. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, No.58 Zhongshan Road 2, Guangzhou, 510080, People’s Republic of China

    Wei Wang, Jian-Chao Zhang, Wen-Shuo Tian, Li-Da Chen, Hang-Tong Hu, Shan-Shan Wu, Xiao-Yan Xie, Ming-De Lu, Ming Kuang & Si-Min Ruan

  2. Department of Medical Ultrasonics, Fetal Medical Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China

    Qiao Zheng

  3. Department of General Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China

    Yu Guo

  4. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China

    Ming-De Lu & Ming Kuang

  5. Department of Ultrasound, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Dong Road, Guangzhou, 510060, People’s Republic of China

    Long-Zhong Liu

Authors
  1. Wei Wang
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Contributions

WW, LZL, XYX, MDL, MK, SMR, JCZ contributed to the study conception and design. WST acquired the 2D-SWE images and measurements. WW drew the ROI for ultrasomics. SMR and SSW valuated the reproducibility. HTH and JCZ analyzed the data. SMR and LDC wrote the first draft of manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Long-Zhong Liu or Si-Min Ruan.

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Wang, W., Zhang, JC., Tian, WS. et al. Shear wave elastography-based ultrasomics: differentiating malignant from benign focal liver lesions. Abdom Radiol 46, 237–248 (2021). https://doi.org/10.1007/s00261-020-02614-3

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Keywords

  • Ultrasonography
  • Machine learning
  • Elasticity imaging techniques
  • Liver