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The pharmacokinetic parameters, including K\n                      <jats:sup>trans<\/jats:sup>\n                      , K\n                      <jats:sub>ep<\/jats:sub>\n                      , V\n                      <jats:sub>e<\/jats:sub>\n                      , and V\n                      <jats:sub>p<\/jats:sub>\n                      , were derived from DCE-MRI by utilizing a two-compartment extended Tofts model and a three-dimensional volume of interest. The postoperative pathologic stage was determined in each patient based on the 8th AJCC cancer staging manual. The quantitative DCE-MRI parameters were compared between stage I\u2013II and stage III\u2013IV lesions. Logistic regression analysis was used to determine independent predictors of tumor stages, followed by receiver operating characteristic (ROC) analysis to evaluate the predictive performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The mean K\n                      <jats:sup>trans<\/jats:sup>\n                      , K\n                      <jats:sub>ep<\/jats:sub>\n                      and V\n                      <jats:sub>p<\/jats:sub>\n                      values were significantly lower in stage III\u2013IV lesions compared with stage I\u2013II lesions (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.013, 0.005 and 0.011, respectively). K\n                      <jats:sub>ep<\/jats:sub>\n                      was an independent predictor for the advanced stages as determined by univariate and multivariate logistic analysis. ROC analysis showed that K\n                      <jats:sub>ep<\/jats:sub>\n                      had the highest predictive capability, with a sensitivity of 64.3%, a specificity of 82.6%, a positive predictive value of 81.8%, a negative predictive value of 65.5%, and an accuracy of 72.5%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The quantitative DCE-MRI parameter K\n                      <jats:sub>ep<\/jats:sub>\n                      can be used as a biomarker for predicting pathologic stages of OTSCC.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-020-00516-w","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T05:03:30Z","timestamp":1602824610000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Quantitative dynamic contrast-enhanced MR imaging can be used to predict the pathologic stages of oral tongue squamous cell carcinoma"],"prefix":"10.1186","volume":"20","author":[{"given":"Na","family":"Guo","sequence":"first","affiliation":[]},{"given":"Weike","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Huijun","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Ziliang","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Zehong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Shuqi","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2224-0887","authenticated-orcid":false,"given":"Xiaohui","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"516_CR1","volume-title":"Oral pathology: clinical, pathologic correlations","author":"JA Regezi","year":"2008","unstructured":"Regezi JA, Sciubba JJ, Jordan RCK. 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