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Pediatric Radiology

Open Access 20-11-2023 | Chemotherapy | Original Article

Computed tomography imaging phenotypes of hepatoblastoma identified from radiomics signatures are associated with the efficacy of neoadjuvant chemotherapy

Authors: Yingqian Chen, Matthias F. Froelich, Hishan Tharmaseelan, Hong Jiang, Yuanqi Wang, Haitao Li, Mingyao Tao, Ying Gao, Jifei Wang, Juncheng Liu, Stefan O. Schoenberg, Shiting Feng, Meike Weis

Published in: Pediatric Radiology

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Abstract

Background

Though neoadjuvant chemotherapy has been widely used in the treatment of hepatoblastoma, there still lacks an effective way to predict its effect.

Objective

To characterize hepatoblastoma based on radiomics image features and identify radiomics-based lesion phenotypes by unsupervised machine learning, intended to build a classifier to predict the response to neoadjuvant chemotherapy.

Materials and methods

In this retrospective study, we segmented the arterial phase images of 137 cases of pediatric hepatoblastoma and extracted the radiomics features using PyRadiomics. Then unsupervised k-means clustering was applied to cluster the tumors, whose result was verified by t-distributed stochastic neighbor embedding (t-SNE). The least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and the clusters were visually analyzed by radiologists. The correlations between the clusters, clinical and pathological parameters, and qualitative radiological features were analyzed.

Results

Hepatoblastoma was clustered into three phenotypes (homogenous type, heterogenous type, and nodulated type) based on radiomics features. The clustering results had a high correlation with response to neoadjuvant chemotherapy (P=0.02). The epithelial ratio and cystic components in radiological features were also associated with the clusters (P=0.029 and 0.008, respectively).

Conclusions

This radiomics-based cluster system may have the potential to facilitate the precise treatment of hepatoblastoma. In addition, this study further demonstrated the feasibility of using unsupervised machine learning in a disease without a proper imaging classification system.

Graphical abstract

Appendix
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Metadata
Title
Computed tomography imaging phenotypes of hepatoblastoma identified from radiomics signatures are associated with the efficacy of neoadjuvant chemotherapy
Authors
Yingqian Chen
Matthias F. Froelich
Hishan Tharmaseelan
Hong Jiang
Yuanqi Wang
Haitao Li
Mingyao Tao
Ying Gao
Jifei Wang
Juncheng Liu
Stefan O. Schoenberg
Shiting Feng
Meike Weis
Publication date
20-11-2023
Publisher
Springer Berlin Heidelberg
Published in
Pediatric Radiology
Print ISSN: 0301-0449
Electronic ISSN: 1432-1998
DOI
https://doi.org/10.1007/s00247-023-05793-5