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A prognostic framework is developed using sample entropy to quantify Blood-Oxygen-Level-Dependent (BOLD) complexity, aiming for computational efficiency, reproducibility, robustness to noise, and transferability across cohorts.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/bold-complexity-characterizes-glioblastoma-survival-via-voxel-wise-and-localized-sample-entropy/440901/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/bold-complexity-characterizes-glioblastoma-survival-via-voxel-wise-and-localized-sample-entropy/440901.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-02","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What biomarker is proposed to predict glioblastoma survival?","Question",{"text":112,"@type":113},"The study uses sample entropy (SampEn) of BOLD signal complexity derived from resting-state fMRI as a prognostic biomarker for overall survival.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were BOLD complexity measures evaluated in the study?",{"text":117,"@type":113},"SampEn was assessed at four levels: whole-brain voxelwise, 15 resting state networks (RSNs), a 64-feature autoencoded latent space, and complexity dynamics along the contrast-enhancing (CE) boundary.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the key findings linking BOLD complexity to survival?",{"text":121,"@type":113},"GBM showed reduced global SampEn versus controls. Within RSNs, medial temporal lobe and basal ganglia SampEn inversely correlated with OS, and latent-space-derived Cox risk stratified patients into high- and low-survival groups.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},440901,1790981208,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","Journal of Neuro-Oncology (2026) 176:151  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1060-025-05361-x  \nRESEARCH  \nBOLD complexity characterizes glioblastoma survival via voxel-wise and localized sample entropy  \nMengqi Gu1 · Patrick H. Luckett2 · Michael Olufawo2 · Donna Dierker3 · Gabriel T. Verastegui2 · Joshua S. Shimony3 · Eric C. Leuthardt2,4,5,6,7,8,9  \nReceived: 22 October 2025 / Accepted: 27 November 2025 / Published online: 5 January 2026 © The Author(s) 2025  \nAbstract  \nPurpose Glioblastoma (GBM) is the most prevalent and lethal primary brain tumor. Non-invasive presurgical biomarkers are urgently needed to predict patients’overall survival (OS). Here we demonstrated a nuanced prognostic tool using sample entropy to assess Blood-Oxygen-Level-Dependent (BOLD) complexity and predict survival outcome, which is computationally efficient, reproducible, robust to noise, and readily transferable across cohorts.  \nMethods Resting-state fMRI from 205 treatment-naïve GBM patients and 1148 cognitively stable healthy controls were evaluated. Sample entropy (SampEn), a complexity metric, was evaluated in relation to OS at four levels: whole brain voxelwise, 15 resting state networks (RSNs), a 64-feature autoencoded latent space, and complexity dynamics along contrastenhancing (CE) boundary.  \nResults GBM patients showed a significant reduction in global SampEn versus controls (p \u003C 0.001). Among RSNs, medial temporal lobe (MTL) and basal ganglia (BGA) SampEn correlated inversely with OS (R² = 0.033 and 0.034; p = 0.008 and 0.006). The latent-space-dependent Cox risk score stratifies patients into high and low survival populations (p \u003C 0.001) . The number of SampEn peaks at the CE boundary also correlated negatively with OS (R² = 0.020, p = 0.037) .  \nConclusions Voxel-wise SampEn revealed widespread loss of BOLD complexity in GBM. It identifies influences at RSNsand tumor-edge, characterizing survival. Latent space analysis revealed whole-brain SampEn characteristics, which provide a compact, data-driven biomarker that augments conventional Cox modelling and stratifies the patient survival. These findings show fMRI-derived SampEn measures are efficient and robust for risk stratification and mechanistic insight in glioblastoma.  \n􀀍 Mengqi Gu [alex.gu@wustl.edu](alex.gu@wustl.edu)  \nPatrick H. Luckett  \n[luckett.patrick@wustl.edu](luckett.patrick@wustl.edu)  \nMichael Olufawo  \n[molufawo@wustl.edu](molufawo@wustl.edu)  \nDonna Dierker  \n[donna@wustl.edu](donna@wustl.edu)  \n[Gabriel T](Gabriel T). Verastegui  \n[g.trevinoverastegui@wustl.edu](g.trevinoverastegui@wustl.edu)  \n[Joshua S. Shimony](Joshua S. Shimony)  \n[shimonyj@wustl.edu](shimonyj@wustl.edu)  \nEric C. Leuthardt  \n[leuthardte@wustl.edu](leuthardte@wustl.edu)  \n1 Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, USA  \n2 Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA  \n3 Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA  \n4 Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA  \n5 Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA  \n6 Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, St. Louis, MO 63130, USA  \n7 Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO 63110, USA  \n8 Brain Laser Center, Washington University School of Medicine, St. Louis, MO 63110, USA  \n9 National Center for Adaptive Neurotechnologies, Albany, USA  \nKeywords Glioblastoma · Functional MRI · Survival · Biomarker · Neuroimaging  \nIntroduction  \nGlioblastoma (GBM) is the most prevalent and aggressive primary brain tumor in adults, with an extremely poor prognosis [1, 2] . Even with maximal resection and chemoradiotherapy, ","cbCaiosqHzsD9JzV","https://ap.wps.com/l/cbCaiosqHzsD9JzV","pdf",1417143,"English","# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Clinical need for presurgical biomarkers\n## Prior rs-fMRI biomarker approaches\n## Rationale for BOLD signal complexity and sample entropy","[{\"question\":\"What biomarker is proposed to predict glioblastoma survival?\",\"answer\":\"The study uses sample entropy (SampEn) of BOLD signal complexity derived from resting-state fMRI as a prognostic biomarker for overall survival.\"},{\"question\":\"How were BOLD complexity measures evaluated in the study?\",\"answer\":\"SampEn was assessed at four levels: whole-brain voxelwise, 15 resting state networks (RSNs), a 64-feature autoencoded latent space, and complexity dynamics along the contrast-enhancing (CE) boundary.\"},{\"question\":\"What were the key findings linking BOLD complexity to survival?\",\"answer\":\"GBM showed reduced global SampEn versus controls. Within RSNs, medial temporal lobe and basal ganglia SampEn inversely correlated with OS, and latent-space-derived Cox risk stratified patients into high- and low-survival groups.\"}]","BOLD complexity characterizes glioblastoma survival via voxel-wise and localized sample entropy | PDF",1790693858,25]