{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:05:11Z","timestamp":1789621511631,"version":"build-2803163510"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Using multimodal MRI, we developed a predictive model for identifying glioma recurrence and treatment response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Clinical data were retrospectively collected on 274 patients with gliomas following surgery. All patients underwent the MRI scan, including Contrast-enhanced T1 Weighted Imaging (CE-T1WI), Arterial Spin Labeling (ASL), Amide Proton Transfer Weighted (APTW) imaging, Quantitative Susceptibility Mapping (QSM), and Diffusion-Weighted Imaging (DWI). The radiomics features extracted from these images were used to construct five single sequence models, one combination model, and a Nomogram, aiming to compare the performance of various models in recognizing glioma recurrence and treatment response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>As a result of combining age, maximum tumor diameter, and IDH1 genotype, a clinical model was constructed, and the area under the curve (AUC) value in the training cohort was 0.748, validation cohort was 0.721 and temporal test cohort was 0.706. The AUC for the five single-sequence models ranged from 0.781-0.899 in the training cohort, from 0.705-0.789 in the validation cohort and from 0.708-0.828 in temporal test cohort. The AUC of the combined model was 0.927, 0.934 and 0.968 in the training cohort, validation cohort and temporal test cohort, respectively. The AUC of the nomogram was 0.983 in the training cohort, 0.958 in the validation cohort and 0.972 in the temporal test cohort. The combination and nomogram models outperformed the clinical and single-sequence models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The combined radiomics model and nomogram showed improved performance in differentiating glioma recurrence from response to treatment compared with the single-sequence models. The nomogram showed discrimination comparable to that of the combined model and may provide additional clinical value for individualized assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1956583","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T04:50:59Z","timestamp":1789620659000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiparametric MRI radiomics integrating Arterial Spin Labeling, Quantitative Susceptibility Mapping, and Amide Proton Transfer imaging for differentiating glioma recurrence from post-treatment response"],"prefix":"10.3389","volume":"16","author":[{"given":"Qi","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Nuclear Medicine, Jeonbuk National University Medical School and Hospital","place":["Jeonju, Republic of 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