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<p>Radiomic features, quantitative descriptors of tumor shape, texture, and intensity derived from MRI serve as powerful non-invasive biomarkers for glioma characterization and prognosis. We present Radiology of Glioma (RadGLO), an interactive platform that leverages these features across multi-institutional datasets (TCGA, UCSF, UPENN) to support grade-wise analysis, gene correlation, and survival prediction. RadGLO integrates two in-house developed modules, RaSPr for risk stratification and TumorVQ for region-specific tumor volume quantification, which also supports user-uploaded MRI data. By enabling personalized prognosis and aiding treatment planning, RadGLO offers a valuable resource that is openly accessible at <a href="https://project.iith.ac.in/cgntlab/radglo/">https://project.iith.ac.in/cgntlab/radglo/</a>.</p>

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RadGLO: an interactive platform for radiomic feature analysis and prognostic modeling in glioma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月14日

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  • Kavita Kundal,
  • K Divya Rani,
  • Vinodini D,
  • Neeraj Kumar,
  • Rahul Kumar

摘要

Radiomic features, quantitative descriptors of tumor shape, texture, and intensity derived from MRI serve as powerful non-invasive biomarkers for glioma characterization and prognosis. We present Radiology of Glioma (RadGLO), an interactive platform that leverages these features across multi-institutional datasets (TCGA, UCSF, UPENN) to support grade-wise analysis, gene correlation, and survival prediction. RadGLO integrates two in-house developed modules, RaSPr for risk stratification and TumorVQ for region-specific tumor volume quantification, which also supports user-uploaded MRI data. By enabling personalized prognosis and aiding treatment planning, RadGLO offers a valuable resource that is openly accessible at https://project.iith.ac.in/cgntlab/radglo/.