[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-360297-105":59,"doc-detail-360297-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","predicting-progression-free-survival-in-glioblastoma-with-neuroimaging-and-machine-learning","Predicting progression-free survival in glioblastoma with neuroimaging and machine learning","","Glioblastoma (GBM) is an aggressive malignant glioma for which reliable estimation of progression-free survival (PFS) before treatment can strengthen clinical decisions and improve patient care. A retrospective cohort of 45 GBM patients underwent structural and resting-state functional MRI prior to surgery. Deep neural networks trained on resting-state functional connectivity and MRMR feature selection predicted individual PFS, showing significant associations with sex, survival-related factors, and MGMT methylation status. Leave-one-out validation achieved high predictive accuracy (R² 0.96).",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-progression-free-survival-in-glioblastoma-with-neuroimaging-and-machine-learning/360297/",{"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/predicting-progression-free-survival-in-glioblastoma-with-neuroimaging-and-machine-learning/360297.png","ImageObject",300,407,{"name":92,"@type":93},"Pentious","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is predicting progression-free survival (PFS) important in glioblastoma?","Question",{"text":112,"@type":113},"PFS is used to measure treatment efficacy, and predicting it before surgical intervention can improve prognostic accuracy and guide clinical decision-making.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and modeling approach were used to predict PFS?",{"text":117,"@type":113},"The study used resting-state functional connectivity from presurgical resting-state fMRI, training deep neural networks and applying feature selection (including MRMR) to identify the strongest predictors.",{"name":119,"@type":110,"acceptedAnswer":120},"Which factors were significantly associated with PFS in the results?",{"text":121,"@type":113},"Reported significant associations included sex, overall survival, MGMT promoter methylation status, presentation of weakness, and presentation of memory impairment.","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},360297,1790454785,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1374404730887,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Journal of Neuro-Oncology (2026) 178:29  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1060-026-05650-z  \nRESEARCH  \nPredicting progression-free survival in glioblastoma with neuroimaging and machine learning  \nDavin A. Hickman-Chow1,3 · Patrick H. Luckett1 · Michael Olufawo1 · Donna Dierker2 · Joshua S. Shimony2 ·  \nEric C. Leuthardt1,4,5,6,7,8  \nReceived: 17 April 2026 / Accepted: 25 May 2026 / Published online: 28 May 2026 © The Author(s) 2026  \nAbstract  \nPurpose Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prior to medical intervention could strengthen clinical decision-making and improve patient care. Here, we utilize machine learning (ML) to predict PFS in GBM patients using resting state network (RSN) connectivity before medical intervention.  \nMethods GBM patients (N= 45, mean age 62.1 ± 10.3 years, mean PFS 9.5 ± 5.6 months, 62.2% male) were retrospectively recruited from Washington University Medical Center. All patients completed structural neuroimaging and resting-state functional MRI before surgery. Deep neural networks were trained on resting-state functional connectivity to predict PFS. Feature selection identified the 15 strongest predictive features prior to training.  \nResults Sex (p = 0.0037), overall survival (p = 0.0003), MGMT promoter methylation status (p = 0.0064), presentation of weakness (p = 0.0037), and presentation of memory impairment (p = 0.045) were significantly associated with PFS. Tumor frequency and spatial correlation analyses associated dorsal attention, visual, frontal-parietal, and default mode networks with shorter PFS. Conversely, right-temporal lobe tumors were associated with better outcomes. RSN spatial maps revealed widespread alterations in association networks in GBM patients relative to controls. MRMR feature selection identified thalamic and association network connectivity, including somatomotor, ventral and dorsal attention, and default mode/parietal memory as the strongest predictors of PFS. Using leave-one-out validation, the model predicted PFS with an RMSE of 1.26 months, MAE of 1.08 months, and R² of 0.96 (p \u003C 0.001) .  \nConclusions Our findings indicate that GBM alters functional brain organization on a widespread scale, and these global effects are informative of patient outcomes.  \nKeywords Deep learning · Glioblastoma · Functional MRI · Progression-Free Survival · Functional Connectivity  \nDavin A. Hickman-Chow and Patrick H. Luckett are co-first authors.  \n􀀍 DavinA. Hickman-Chow [D.a.hickman-chow@wustl.edu](D.a.hickman-chow@wustl.edu)  \n􀀍 Patrick H. Luckett [luckett.patrick@wustl.edu](luckett.patrick@wustl.edu)  \n1 Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA  \n2 Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA  \n3 Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA  \n4 Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA  \n5 Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, St. Louis, MO 63130, USA  \n6 Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO 63110, USA  \n7 Brain Laser Center, Washington University School of Medicine, St. Louis, MO 63110, USA  \n8 National Center for Adaptive Neurotechnologies, 660 South Euclid Avenue Campus Box 8057, St. Louis, MO  \n63110, USA  \nIntroduction  \nGlioblastoma (GBM) is a primary central nervous system malignancy accounting for approximately 80% of all primary malignant brain tumors in adults, with a reported oneyear overall survival around 68% with the highest incidence in patients ≥ 65 years [1] . Despite ongoing efforts to produce novel therapeutic approaches, survival improvements remain minimal, alongside var","cbCaie8OfMbCLAQW","https://ap.wps.com/l/cbCaie8OfMbCLAQW","pdf",2354489,11,"English","# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"Why is predicting progression-free survival (PFS) important in glioblastoma?\",\"answer\":\"PFS is used to measure treatment efficacy, and predicting it before surgical intervention can improve prognostic accuracy and guide clinical decision-making.\"},{\"question\":\"What data and modeling approach were used to predict PFS?\",\"answer\":\"The study used resting-state functional connectivity from presurgical resting-state fMRI, training deep neural networks and applying feature selection (including MRMR) to identify the strongest predictors.\"},{\"question\":\"Which factors were significantly associated with PFS in the results?\",\"answer\":\"Reported significant associations included sex, overall survival, MGMT promoter methylation status, presentation of weakness, and presentation of memory impairment.\"}]","Predicting progression-free survival in glioblastoma with neuroimaging and machine learning | PDF",1790139515,28]