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Publication Detail
Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.
  • Publication Type:
    Journal article
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  • Authors:
    Sudre CH, Panovska-Griffiths J, Sanverdi E, Brandner S, Katsaros VK, Stranjalis G, Pizzini FB, Ghimenton C, Surlan-Popovic K, Avsenik J, Spampinato MV, Nigro M, Chatterjee AR, Attye A, Grand S, Krainik A, Anzalone N, Conte GM, Romeo V, Ugga L, Elefante A, Ciceri EF, Guadagno E, Kapsalaki E, Roettger D, Gonzalez J, Boutelier T, Cardoso MJ, Bisdas S
  • Publication date:
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  • Journal:
    BMC Med Inform Decis Mak
  • Volume:
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  • Keywords:
    Diagnostic machine learning, Glioma stratification, Isocitrate dehydrogenase; DSC-MRI
BACKGROUND: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. METHODS: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. RESULTS: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1). CONCLUSIONS: Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.
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