{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:58:56Z","timestamp":1790283536885,"version":"4.1.0"},"reference-count":43,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"3","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      High-grade gliomas exhibit substantial spatial and temporal heterogeneity, posing a challenge for radiotherapy (RT) target definition. Adaptive RT, which prospectively modifies treatment plans, may benefit from early identification of intratumoral regions likely to exhibit differential treatment response. Apparent diffusion coefficient (\n                      <jats:italic toggle=\"yes\">ADC<\/jats:italic>\n                      ), a marker associated with tumor cellularity and aggressiveness, is a promising imaging biomarker of RT response. We present a patient-specific computational framework that predicts treatment response and identifies candidate adaptive RT targets from anticipated spatiotemporal patterns of\n                      <jats:italic toggle=\"yes\">ADC<\/jats:italic>\n                      changes (\u0394\n                      <jats:italic toggle=\"yes\">ADC<\/jats:italic>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Using longitudinal multiparametric magnetic resonance imaging (MRI) from 21 patients, the mathematical model was calibrated to spatiotemporally forecast tumor cell density immediately after and 1-month post-RT. Predictions of tumor cell density were converted to\n                      <jats:italic toggle=\"yes\">ADC<\/jats:italic>\n                      maps. \u0394\n                      <jats:italic toggle=\"yes\">ADC<\/jats:italic>\n                      from baseline was analyzed using Pareto tail analysis. The lower \u0394\n                      <jats:italic toggle=\"yes\">ADC<\/jats:italic>\n                      distribution tail was used to classify tumor voxels as regions of increased diffusion restriction (IDR) or decreased\/maintained diffusion restriction (DMDR). We compared agreement in categories between measured and predicted \u0394\n                      <jats:italic toggle=\"yes\">ADC<\/jats:italic>\n                      from MRI data. Classification performance was assessed using receiver operating characteristic (ROC) analysis across four scenarios combining two time points and two image inclusion strategies (\u00b1 nonenhancing tumor volume).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The framework achieved a median ROC AUC of 0.88 (0.80-0.93), indicating strong performance in distinguishing IDR from DMDR regions. No significant differences were observed between forecasting time points (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      value: .08) or image inclusion strategies (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      value: .50).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This framework can delineate IDR and DMDR regions, offering a potential strategy for prospectively defining candidate biologically informed adaptive RT targets. Future studies are needed to evaluate their role in adaptive RT planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-25-00336","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:00:28Z","timestamp":1790280028000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Patient-Specific Computational Framework to Spatiotemporally Forecast Treatment Response and Identify Biologically Informed Radiotherapy Targets in High-Grade Glioma"],"prefix":"10.1200","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5193-2111","authenticated-orcid":true,"given":"Sophia","family":"Ty","sequence":"first","affiliation":[{"name":"Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, 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