ArticleScientific reports2018
Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study.
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30 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
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- [An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.BMC medical imaging · 2024Article
- A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients.Neuroradiology · 2024Article
- Phenomic Imaging.Phenomics (Cham, Switzerland) · 2023Review
- Diagnostic value of MRI radiomics in differentiating high‑grade glioma from low‑grade glioma: A meta‑analysis.Oncology letters · 2023Article
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- The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma.Translational cancer research · 2022Article
- The application of radiomics in predicting gene mutations in cancer.European radiology · 2022Review
- Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading.Journal of clinical medicine · 2022Article
- Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic MRI.Scientific reports · 2022Article
- Radiomic Features Associated with Extent of Resection in Glioma Surgery.Acta neurochirurgica. Supplement · 2022Review
- Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging.Insights into imaging · 2021Review
- Reverse Engineering Glioma Radiomics to Conventional Neuroimaging.Neurologia medico-chirurgica · 2021Review
- Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas.Frontiers in oncology · 2021Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status.
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