{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T22:55:53Z","timestamp":1788648953383,"version":"build-2803163510"},"reference-count":66,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100006510","name":"Duke University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006510","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study investigated the prognostic potential of intra\u2010treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20\u00a0Gy is associated with recurrence\u2010free survival (RFS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>Sixty\u2010four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB\u2010approved study. Investigational <jats:sup>18<\/jats:sup>F\u2010FDG\u2010PET\/CT images were acquired prior to treatment and 2\u00a0weeks (20\u00a0Gy) into a seven\u2010week course of therapy. Fifty\u2010five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were na\u00efvely compared to residual disease and\/or subsequent recurrence and used to derive Kaplan\u2010Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>While pre\u2010treatment radiomics were not prognostic, intra\u2010treatment radiomic expression was intrinsically associated with both residual\/recurrent disease (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.0256, <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mp14926-math-0001.png\" xlink:title=\"urn:x-wiley:00942405:media:mp14926:mp14926-math-0001\"\/> test) and RFS (HR\u00a0=\u00a07.53, 95% CI\u00a0=\u00a02.54\u201322.3; <jats:italic>P<\/jats:italic>\u00a0=\u00a00.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR\u00a0=\u00a02.70; 95% CI\u00a0=\u00a01.26\u20135.76; <jats:italic>P<\/jats:italic>\u00a0=\u00a00.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20\u00a0Gy (adjusted HR\u00a0=\u00a02.69; 95% CI\u00a0=\u00a01.03\u20137.04; <jats:italic>P<\/jats:italic>\u00a0=\u00a00.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20\u00a0Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our data illustrate the prognostic value of intra\u2010treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow\u2010up times, and unsupervised clustering formalism that is less prone to hyper\u2010parameter tuning and over\u2010fitting compared to supervised learning.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14926","type":"journal-article","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T14:00:09Z","timestamp":1620482409000},"page":"3767-3777","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers"],"prefix":"10.1002","volume":"48","author":[{"given":"Kyle J.","family":"Lafata","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Duke University School of Medicine Durham NC USA"},{"name":"Department of Radiology Duke University School of Medicine Durham NC USA"},{"name":"Department of Electrical &amp; Computer Engineering Duke University Pratt School of Engineering Durham NC USA"},{"name":"Medical Physics Graduate Program Duke University Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yushi","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University School of Medicine Durham NC USA"},{"name":"Medical Physics Graduate Program Duke University Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University School of Medicine Durham NC USA"},{"name":"Medical Physics Graduate Program Duke University Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yvonne M.","family":"Mowery","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University School of Medicine Durham NC USA"},{"name":"Department of Head and Neck Surgery &amp; Communication Sciences Duke University School of Medicine Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irina","family":"Vergalasova","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School New Brunswick NJ USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donna","family":"Niedzwiecki","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David S.","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University School of Medicine Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian\u2010Guo","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mathematics Duke University Durham NC USA"},{"name":"Department of Physics Duke University Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David M.","family":"Brizel","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University School of Medicine Durham NC USA"},{"name":"Department of Head and Neck Surgery &amp; Communication Sciences Duke University School of Medicine Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang\u2010Fang","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University School of Medicine Durham NC USA"},{"name":"Medical Physics Graduate Program Duke University Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,6,2]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa0912217"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2014.58.6412"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(15)00560-4"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2007.12.7597"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2011.36.4596"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.19.00441"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2019.02.008"},{"key":"e_1_2_8_9_1","first-page":"34118","article-title":"Early 18F\u2010FDG\u2010PET response during radiation therapy for HPV\u2010related oropharyngeal cancer may predict disease recurrence","volume":"0360","author":"Mowery YM","year":"2020","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1602252"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)32591-7"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32752-1"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32779-X"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5006"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa73cc"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-10371-5"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.10.023"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-14687-0"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/0284186X.2017.1346382"},{"key":"e_1_2_8_20_1","first-page":"272","article-title":"Radiomic machine\u2010learning classifiers for prognostic biomarkers of head and neck cancer","volume":"3","author":"Parmar C","year":"2015","journal-title":"Front Oncol"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2017.06.015"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2017.06.004"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2017.03.004"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.08.024"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2017.02.006"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2017.10.008"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2018.06.012"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2018.00035"},{"key":"e_1_2_8_29_1","unstructured":"ZwanenburgA LegerS VallieresM LockS.Image biomarker standardisation initiative reference manual. arXiv preprint arXiv:1612.07003."},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020191145"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.113.129858"},{"key":"e_1_2_8_32_1","article-title":"Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes","volume":"6","author":"Chang Y","year":"2020","journal-title":"Biomed Phys Eng Express"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1090\/qam\/1521"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01830"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"e_1_2_8_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/83.725367"},{"key":"e_1_2_8_37_1","first-page":"140","article-title":"Texture indexes and gray level size zone matrix. 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