{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T22:40:14Z","timestamp":1792190414055,"version":"4.4.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["UG3\/UH3 CA232820"],"award-info":[{"award-number":["UG3\/UH3 CA232820"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R01 CA290435"],"award-info":[{"award-number":["R01 CA290435"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R01 CA294647"],"award-info":[{"award-number":["R01 CA294647"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["P30CA068485"],"award-info":[{"award-number":["P30CA068485"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Department of Radiology at the University of Alabama at Birmingham","award":["RRIPA"],"award-info":[{"award-number":["RRIPA"]}],"id":[{"id":"https:\/\/ror.org\/008s83205","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To prospectively evaluate whether quantitative DCE-MRI can predict early chemotherapy response in pancreatic ductal adenocarcinoma (PDAC), and to assess the added value of a point-of-care portable perfusion phantom (P4) for correcting scanner-dependent errors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Fifty-four patients with biopsy-confirmed PDAC were prospectively enrolled at two institutions; 42 completed MRI scans at baseline (1\u2009\u00b1\u20091 weeks before chemotherapy) and 7\u2009\u00b1\u20092 weeks after treatment initiation. P4s were imaged concurrently to correct for scanner-dependent measurement errors. Eleven pharmacokinetic (PK) parameters were derived using Tofts, extended Tofts, and shutter speed models. Treatment response at 17\u2009\u00b1\u20094 weeks was assessed using the modified RECIST, with responders defined as patients with &gt;\u200910% tumor size reduction. Early changes in PK parameters were analyzed using receiver operating characteristic curves to assess predictive accuracy, and differences between responders and non-responders were assessed using one-way ANOVA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 42 patients (median age 67 years; 27 males), 20 were responders and 22 non-responders. Early changes in\n                      <jats:italic>K<\/jats:italic>\n                      <jats:sup>trans<\/jats:sup>\n                      (\u0394\n                      <jats:italic>K<\/jats:italic>\n                      <jats:sup>trans<\/jats:sup>\n                      ) significantly distinguished responders from non-responders across all PK models, achieving ~80% predictive accuracy before P4 correction. After P4-based correction, \u0394\n                      <jats:italic>K<\/jats:italic>\n                      <jats:sup>trans<\/jats:sup>\n                      accuracy improved to 98% with the extended Tofts model. In contrast, standard RECIST-based CT assessment at 8\u2009\u00b1\u20092 weeks identified response in only 17% of patients.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Quantitative DCE-MRI enables early, highly accurate prediction of chemotherapy response in PDAC, and P4-based error correction further enhances reliability. This approach offers a prospectively validated imaging biomarker that can support timely, personalized treatment decisions for patients with PDAC.<\/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 quantitative dynamic contrast-enhanced MRI accurately identify early chemotherapy response in PDAC before conventional morphologic changes become apparent on CT?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>P4-corrected quantitative DCE-MRI predicted 17-week tumor response with up to 98% accuracy after only 7 weeks of chemotherapy treatment<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>P4-corrected quantitative DCE-MRI may enable earlier identification of ineffective chemotherapy in pancreatic cancer, supporting timely treatment modification, reducing unnecessary toxicity, and facilitating more personalized patient management<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12810-2","type":"journal-article","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T06:02:44Z","timestamp":1787378564000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantitative dynamic contrast-enhanced magnetic resonance imaging for early assessment of treatment response in pancreatic ductal adenocarcinoma"],"prefix":"10.1007","author":[{"given":"Martin D.","family":"Holland","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ezinwanne E.","family":"Onuoha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darryl","family":"Outlaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet","family":"Akce","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Midhun","family":"Malla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salila","family":"Hashmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sushanth","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. 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Dr. Grant Williams reports serving as a speaker for Takeda Pharmaceuticals and as an advisory board member for GlaxoSmithKline. Dr. Bassel El-Rayes reports institutional research support from Bristol Myers Squibb, Xencor, Merck Sharp & Dohme, Exelixis, GlaxoSmithKline, and AstraZeneca; consulting or advisory roles with AstraZeneca and Genentech; and participation in a speaker bureau for Seagen. Dr. Mehmet Akce has served on scientific advisory boards for several companies, including Bristol Myers Squibb\u2013Ono Pharmaceutical (institutional), GSK (institutional), Genentech (institutional), Bayer (institutional), Relay Therapeutics (institutional), ProDa Biotech (institutional), Exelixis (institutional), and AstraZeneca (institutional). Dr. Akce has also held consulting or advisory roles with Eisai, Ipsen, Exelixis, GSK, QED Therapeutics, Isofol, Curio Science, AstraZeneca, Genentech, Incyte, Taiho Oncology, Daiichi Sankyo, Bristol Myers Squibb, and Partner Therapeutics.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No complex statistical methods were required for this study. The statistical analyses were performed by the co-first author, Ezinwanne Onuoha, using SAS software and were independently reviewed and validated by the corresponding author, Harrison Kim, who has over 20 years of experience in statistical analysis.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Written informed consent was obtained from all subjects (patients) in this study.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Data from a small pilot cohort (\n                      n\n                      \u2009=\u20098) were previously reported in Investigative Radiology (2019, PMID: 30138218). The present study substantially extends that work by prospectively enrolling an additional 46 patients (total\n                      n\n                      \u2009=\u200954), enabling robust validation of predictive performance and assessment of clinical applicability.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}