ArticleThe British journal of radiology2022
Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: a study on the selection of optimal multiple sequences and multiregions.
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Differentiating medulloblastoma and pilocytic astrocytoma in children based on multimodal MRI radiomics model.Neuroradiology · 2026Article
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Precise diagnosis of pediatric posterior cranial fossa neoplasms based on 2.5D MRI deep learning.Frontiers in oncology · 2025Article
- A bibliometric analysis of research trends and hotspots of pilocytic astrocytoma from 2004 to 2023.Neurosurgical review · 2024Article
- Advanced imaging techniques and non-invasive biomarkers in pediatric brain tumors: state of the art.Neuroradiology · 2024Review
- Review
- Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature.Cancers · 2023Review
- Prediction of angiogenesis in extrahepatic cholangiocarcinoma using MRI-based machine learning.Frontiers in oncology · 2023Article
- Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review.Cancers · 2022Review
- Review
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9 authors.
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Abstract
objectiveTo evaluate the diagnostic performance of a radiomics model based on multiregional and multiparametric MRI to classify paediatric posterior fossa tumours (PPFTs), explore the contribution of different MR sequences and tumour subregions in tumour classification, and examine whether contrast-enhanced
methodsThis retrospective study of 136 PPFTs extracted 11,958 multiregional (enhanced, non-enhanced, and total tumour) features from multiparametric MRI (
resultsThe established model achieved an overall area under the curve (AUC) of 0.977 in the validation cohort. The performance of PPFTs significantly improved after replacing T1C with apparent diffusion coefficient maps added into the plain scan sequences (AUC from 0.812 to 0.917). When oedema features were added to contrast-enhancing tumour volume, the performance did not significantly improve.
conclusionThe radiomics model built by multiregional and multiparametric MRI features allows for the excellent distinction of different PPFTs and provides valuable references for the rational adoption of MR sequences. ADVANCES IN KNOWLEDGE: This study emphasized that T1C has limited added value in predicting PPFTs and should be cautiously adopted. Selecting optimal MR sequences may help guide clinicians to better allocate acquisition sequences and reduce medical costs.
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