ArticlePediatric radiology2024
Computed tomography imaging phenotypes of hepatoblastoma identified from radiomics signatures are associated with the efficacy of neoadjuvant chemotherapy.
Article in Pediatric radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Quantitative analysis of tumor perfusion via contrast-enhanced ultrasound to predict the neoadjuvant chemotherapy efficacy for children with hepatoblastoma.Journal of ultrasound · 2026Article
- Hepatoblastoma.Nature reviews. Disease primers · 2025Review
- Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics.Journal of cancer research and clinical oncology · 2024Article
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Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThough neoadjuvant chemotherapy has been widely used in the treatment of hepatoblastoma, there still lacks an effective way to predict its effect.
objectiveTo 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
methodsIn 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.
resultsHepatoblastoma 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).
conclusionsThis 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.
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