ArticleFrontiers in neurology2023
Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Adaptive Vision-Language Transformer for Multimodal CNS Tumor Diagnosis.Biomedicines · 2025Article
- Explainable Radiomics-Based Model for Automatic Image Quality Assessment in Breast Cancer DCE MRI Data.Journal of imaging · 2025Article
- Machine Learning-Based Prediction of GliomaCancers · 2024Article
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7 authors.
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Abstract
Objective: This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2-4) from 3 different centers. Methods: To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results: The Mean slope of increase (MSI) and the K Conclusion: The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.
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