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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      GBM patients (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200945, mean age 62.1\u2009\u00b1\u200910.3 years, mean PFS 9.5\u2009\u00b1\u20095.6 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Sex (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0037), overall survival (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0003), MGMT promoter methylation status (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0064), presentation of weakness (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0037), and presentation of memory impairment (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.045) 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 R\u00b2 of 0.96 (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings indicate that GBM alters functional brain organization on a widespread scale, and these global effects are informative of patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11060-026-05650-z","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T16:03:35Z","timestamp":1779984215000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting progression-free survival in glioblastoma with neuroimaging and machine learning"],"prefix":"10.1007","volume":"178","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0883-1477","authenticated-orcid":false,"given":"Davin A.","family":"Hickman-Chow","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2262-6605","authenticated-orcid":false,"given":"Patrick H.","family":"Luckett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Olufawo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donna","family":"Dierker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua S.","family":"Shimony","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric C.","family":"Leuthardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"5650_CR1","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s11060-017-2619-1","volume":"135","author":"C Kelly","year":"2017","unstructured":"Kelly C, Majewska P, Ioannidis S et al (2017) Estimating progression-free survival in patients with glioblastoma using routinely collected data. 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The IRB waived the requirement for informed consent for this retrospective study. This manuscript contains no individual person\u2019s data in any form.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate\/publish"}},{"value":"Patrick Luckett, Joshua Shimony, Eric Leuthardt, and\/or Washington University in St. Louis may receive royalty income based on a technology developed by Patrick Luckett, Joshua Shimony, and Eric Leuthardt and licensed by Washington University to Sora Neuroscience. Eric Leuthardt owns stock in Neurolutions, General Sensing, Face to Face Biometrics, Caeli Vascular, Acera, Sora Neuroscience, Inner Cosmos, Inflexion Vascular, and Petal Surgical. Washington University has equity ownership of Neurolutions. The remaining authors declare no financial interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"29"}}