{"title":"RadGLO: an interactive platform for radiomic feature analysis and prognostic modeling in glioma.","authors":"Kavita Kundal, K Divya Rani, Vinodini D, Neeraj Kumar, Rahul Kumar","doi":"10.1038/s41698-025-01124-z","DOIUrl":null,"url":null,"abstract":"<p><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 https://project.iith.ac.in/cgntlab/radglo/ .</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"323"},"PeriodicalIF":6.8000,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521371/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Precision Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41698-025-01124-z","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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/ .
期刊介绍：
Online-only and open access, npj Precision Oncology is an international, peer-reviewed journal dedicated to showcasing cutting-edge scientific research in all facets of precision oncology, spanning from fundamental science to translational applications and clinical medicine.
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