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A spatiotemporal probabilistic model was used to create synthetic PK maps. Structural Similarity Index Measure (SSIM) to ground truth (GT) maps were calculated. Reliability was evaluated using the intraclass correlation coefficient (ICC) for synthetic and GT PK maps. For clinical validation, Area Under the Receiver Operating Characteristic Curve (AUROC) was obtained for predicting WHO low vs high grade and IDH-wildtype vs mutant.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>329 patients (mean age, 55\u2009\u00b1\u200915 years, 197 men) were eligible. Synthetic K<jats:sup>trans<\/jats:sup>, Vp, Ve maps showed high SSIM (0.961, 0.962, 0.890) compared to the GT maps. The ICC of PK maps was significantly higher in synthetic PK maps compared to the conventional approach: 1.00 vs 0.68 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) for K<jats:sup>trans<\/jats:sup>, 1.00 vs 0.59 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) for Vp, 1.00 vs 0.64 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) for Ve. PK values of enhancing tumor portion obtained from synthetic and GT maps were comparable in AUROC: (1) K<jats:sup>trans<\/jats:sup>, 0.857 vs 0.842 (<jats:italic>p<\/jats:italic>\u2009=\u20090.57); Vp, 0.864 vs 0.835 (<jats:italic>p<\/jats:italic>\u2009=\u20090.31); and Ve, 0.835 vs 0.830 (<jats:italic>p<\/jats:italic>\u2009=\u20090.88) for mutation prediction. (2) K<jats:sup>trans<\/jats:sup>, 0.934 vs 0.907 (<jats:italic>p<\/jats:italic>\u2009=\u20090.50); Vp, 0.927 vs 0.899 (<jats:italic>p<\/jats:italic>\u2009=\u20090.24); and Ve, 0.945 vs 0.910 (<jats:italic>p<\/jats:italic>\u2009=\u20090.24) for glioma grading.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Synthetic PK maps generated from DCE-MRI using a spatiotemporal probabilistic deep-learning model showed improved reliability without compromising diagnostic performance in glioma grading.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Key Points<\/jats:title>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Question<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>Can a deep learning model enhance the reliability of dynamic contrast-enhanced MRI (DCE-MRI) for more consistent and clinically acceptable glioma imaging?<\/jats:italic>\n            <\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Findings<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>A spatiotemporal deep learning model outperformed the Tofts model in Ktrans reliability and preserved diagnostic performance for IDH mutation and glioma grade, bypassing arterial input function estimation<\/jats:italic>.<\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Clinical relevance<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>Enhancing DCE-MRI reliability with deep learning improves imaging consistency, supports molecular tumor characterization through reproducible pharmacokinetic maps, and enables personalized treatment planning, which might lead to better clinical outcomes for patients with diffuse gliomas<\/jats:italic>.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1007\/s00330-025-11588-z","type":"journal-article","created":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T09:40:56Z","timestamp":1745055656000},"page":"6229-6239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep learning enhances reliability of dynamic contrast-enhanced MRI in diffuse gliomas: bypassing post-processing and providing uncertainty maps"],"prefix":"10.1007","volume":"35","author":[{"given":"Young Wook","family":"Lyoo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haneol","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhyeok","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung Hyun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inpyeong","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Wook","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung Hong","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaejun","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5175-3307","authenticated-orcid":false,"given":"Kyu Sung","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,19]]},"reference":[{"key":"11588_CR1","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.3174\/ajnr.A3825","volume":"35","author":"SC Jung","year":"2014","unstructured":"Jung SC, Yeom JA, Kim JH et al (2014) Glioma: application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading. 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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The Institutional Review Board (IRB) of Seoul National University Hospital approved this study (No. 2212-077-1385), and written informed consent was waived.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"None of the study subjects or cohorts have been previously reported.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}