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Radiomics-based machine learning approach in differentiating fibro-adipose vascular anomaly from venous malformation

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Abstract

Background

As a complex vascular malformation, fibro-adipose vascular anomaly was first proposed in 2014. Its overlap with other vascular malformations regarding imaging and clinical features often leads to misdiagnosis and improper management.

Objective

To construct a radiomics-based machine learning model to help radiologists differentiate fibro-adipose vascular anomaly from common venous malformations.

Materials and methods

We retrospectively analyzed 178 children, adolescents and young adults with vascular malformations (41 fibro-adipose vascular anomaly and 137 common vascular malformation cases) who underwent MRI before surgery between May 2012 to January 2021. We extracted radiomics features from T1-weighted images and fat-saturated (FS) T2-weighted images and further selected features through least absolute shrinkage and selection operator (LASSO) and Boruta methods. We established eight weighted logistic regression classification models based on various combinations of feature-selection strategies (LASSO or Boruta) and sequence types (single- or multi-sequence). Finally, we evaluated the performance of each model by the mean area under the receiver operating characteristics curve (ROC-AUC), sensitivity and specificity in 10 runs of repeated k-fold (k = 10) cross-validation.

Results

Two multi-sequence models based on axial FS T2-W, coronal FS T2-W and axial T1-W images showed promising performance. The LASSO-based multi-sequence model achieved an AUC of 97%±3.8, a sensitivity of 94%±12.4 and a specificity of 89%±9.0. The Boruta-based multi-sequence model achieved an AUC of 97%±3.7, a sensitivity of 95%±10.5 and a specificity of 87%±9.0.

Conclusion

The radiomics-based machine learning model can provide a promising tool to help distinguish fibro-adipose vascular anomaly from common venous malformations.

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Funding

This study has received funding from the National Key R&D Program of China (2017YFE0103600), the National Natural Science Foundation of China (81720108021) and the Henan provincial science and technology research projects (SB201901070 and 212102310689).

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Authors and Affiliations

  1. Department of Medical Imaging, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003, Henan, China

    Jian Dong, Yaping Wu, Fangfang Fu, Xiaochen Li & Meiyun Wang

  2. Department of Hemangiomas and Vascular Malformations, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China

    Yubin Gong & Changxian Dong

  3. Department of Pathology, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China

    Qiuyu Liu

  4. Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Hui Han

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  1. Jian Dong
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Correspondence to Meiyun Wang.

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Dong, J., Gong, Y., Liu, Q. et al. Radiomics-based machine learning approach in differentiating fibro-adipose vascular anomaly from venous malformation. Pediatr Radiol 53, 404–414 (2023). https://doi.org/10.1007/s00247-022-05520-6

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  • DOI: https://doi.org/10.1007/s00247-022-05520-6

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