ArticleEBioMedicine2020
Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer.Abdominal radiology (New York) · 2023Trial
- An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features.Communications medicine · 2026Article
- Construction and exploratory validation of a digital pathology prognostic model for stage II-III colorectal cancer based on contrastive learning and attention mechanisms.Frontiers in oncology · 2026Article
- Personalized chronotherapy in glioblastoma: integrating circadian profiling and PK-PD modelling to optimize temozolomide timing.NPJ precision oncology · 2025Article
- Pathology-interpretable radiomic model for predicting clinical outcome in patients with osteosarcoma: a retrospective, multicenter study.BMC medicine · 2025Article
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma.European radiology · 2025Article
- Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.Neurosurgery · 2025Review
- Application of Radiomics in Predicting the Prognosis of Medulloblastoma in Children.Children (Basel, Switzerland) · 2025Article
- Exploring a recurrence model for atypical meningioma based on multiparametric MRI radiomic and clinical characteristics: a multicenter retrospective cohort study.Radiation oncology (London, England) · 2025Article
- Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.BMC gastroenterology · 2024Article
- Draw on advantages and avoid disadvantages: CT-derived individualized radiomic signature for predicting chemo-radiotherapy sensitivity in unresectable advanced non-small cell lung cancer.Journal of cancer research and clinical oncology · 2024Article
- Radiomic- and dosiomic-based clustering development for radio-induced neurotoxicity in pediatric medulloblastoma.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
- MR radiomics to predict microvascular invasion status and biological process in combined hepatocellular carcinoma-cholangiocarcinoma.Insights into imaging · 2024Article
- Article
- Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning.European radiology · 2024Article
- Diffusion tensor imaging-based machine learning for IDH wild-type glioblastoma stratification to reveal the biological underpinning of radiomic features.CNS neuroscience & therapeutics · 2023Article
- Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.Diagnostics (Basel, Switzerland) · 2023Review
- CRISPR-Cas9 identifies growth-related subtypes of glioblastoma with therapeutical significance through cell line knockdown.BMC cancer · 2023Article
- Article
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Authors and funding
25 authors.
Funding
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Abstract
backgroundTo develop a radiomics signature for predicting overall survival (OS)/progression-free survival (PFS) in patients with medulloblastoma (MB), and to investigate the incremental prognostic value and biological pathways of the radiomics patterns.
methodsA radiomics signature was constructed based on magnetic resonance imaging (MRI) from a training cohort (n = 83), and evaluated on a testing cohort (n = 83). Key pathways associated with the signature were identified by RNA-seq (GSE151519). Prognostic value of pathway genes was assessed in a public GSE85218 cohort.
findingsThe radiomics-clinicomolecular signature predicted OS (C-index 0.762) and PFS (C-index 0.697) better than either the radiomics signature (C-index: OS: 0.649; PFS: 0.593) or the clinicomolecular signature (C-index: OS: 0.725; PFS: 0.691) alone, with a better calibration and classification accuracy (net reclassification improvement: OS: 0.298, P = 0.022; PFS: 0.252, P = 0.026). Nine pathways were significantly correlated with the radiomics signature. Average expression value of pathway genes achieved significant risk stratification in GSE85218 cohort (log-rank P = 0.016).
interpretationThis study demonstrated radiomics signature, which associated with dysregulated pathways, was an independent parameter conferring incremental value over clinicomolecular factors in survival predictions for MB patients.
fundingA full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
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