{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:06:35Z","timestamp":1753884395153,"version":"3.41.2"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"1119","license":[{"start":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T00:00:00Z","timestamp":1608163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,1]]},"abstract":"<jats:sec>\n                  <jats:title>Objective:<\/jats:title>\n                  <jats:p>To evaluate the effect of artery input function (AIF) derived from different arteries for pharmacokinetic modeling on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in the grading of gliomas.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods:<\/jats:title>\n                  <jats:p>49 patients with pathologically confirmed gliomas were recruited and underwent DCE-MRI. A modified Tofts model with different AIFs derived from anterior cerebral artery (ACA), ipsilateral and contralateral middle cerebral artery (MCA) and posterior cerebral artery (PCA) was used to estimate quantitative parameters such as Ktrans (volume transfer constant) and Ve (fractional extracellular-extravascular space volume) for distinguishing the low grade glioma from high grade glioma. The Ktrans and Ve were compared between different arteries using Two Related Samples Tests (TRST) (i.e. Wilcoxon Signed Ranks Test). In addition, these parameters were compared between the low and high grades as well as between the grade II and III using the Mann-Whitney U-test. A p-value of less than 0.05 was regarded as statistically significant.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results:<\/jats:title>\n                  <jats:p>All the patients completed the DCE-MRI successfully. Sharp wash-in and wash-out phases were observed in all AIFs derived from the different arteries. The quantitative parameters (Ktrans and Ve) calculated from PCA were significant higher than those from ACA and MCA for low and high grades, respectively (p &amp;lt; 0.05). Despite the differences of quantitative parameters derived from ACA, MCA and PCA, the Ktrans and Ve from any AIFs could distinguish between low and high grade, however, only Ktrans from any AIFs could distinguish grades II and III. There was no significant correlation between parameters and the distance from the artery, which the AIF was extracted, to the tumor.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion:<\/jats:title>\n                  <jats:p>Both quantitative parameters Ktrans and Ve calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas, however, only Ktrans can distinguish grades II and III.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge:<\/jats:title>\n                  <jats:p>We sought to assess the effect of AIF on DCE-MRI for determining grades of gliomas. Both quantitative parameters Ktrans and Ve calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1259\/bjr.20200699","type":"journal-article","created":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T23:19:14Z","timestamp":1608247154000},"source":"Crossref","is-referenced-by-count":2,"title":["Effects of artery input function on dynamic contrast-enhanced MRI for determining grades of gliomas"],"prefix":"10.1093","volume":"94","author":[{"given":"Lin","family":"Jia","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Xin Jiang Medical University , Urumqi , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Wuhan University of Technology , Wuhan , China"},{"name":"Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wan","sequence":"additional","affiliation":[{"name":"Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"},{"name":"CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"},{"name":"Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liwen","family":"Wan","sequence":"additional","affiliation":[{"name":"Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"},{"name":"CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"},{"name":"Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxiao","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Xin Jiang Medical University , Urumqi , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9510-4520","authenticated-orcid":false,"given":"Na","family":"Zhang","sequence":"additional","affiliation":[{"name":"Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"},{"name":"CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"},{"name":"Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,12,17]]},"reference":[{"key":"2024050215132059800_b1","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s10143-017-0921-1","article-title":"Predicting complexity of tumor removal and postoperative outcome in patients with high-grade gliomas","volume":"41","author":"Ganau","year":"2018","journal-title":"Neurosurg Rev"},{"key":"2024050215132059800_b2","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.cancergen.2012.10.009","article-title":"Genetics of adult glioma","volume":"205","author":"Goodenberger","year":"2012","journal-title":"Cancer Genet"},{"issue":"suppl_3","key":"2024050215132059800_b3","doi-asserted-by":"crossref","first-page":"iii259","DOI":"10.1093\/neuonc\/noy139.161","article-title":"P01.119 TSPO imaging in high grade glioma - a multitracer PET, MRI and neuropathology study","volume":"20","author":"Agushi","year":"2018","journal-title":"Neuro Oncol"},{"key":"2024050215132059800_b4","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1002\/nbm.3153","article-title":"Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation","volume":"27","author":"Lee","year":"2014","journal-title":"NMR Biomed"},{"key":"2024050215132059800_b5","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","article-title":"The 2016 World Health organization classification of tumors of the central nervous system: a summary","volume":"131","author":"Louis","year":"2016","journal-title":"Acta Neuropathol"},{"key":"2024050215132059800_b6","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1093\/neuonc\/nop019","article-title":"Joint NCCTG and NABTC prognostic factors analysis for high-grade recurrent glioma","volume":"12","author":"Wu","year":"2010","journal-title":"Neuro Oncol"},{"key":"2024050215132059800_b7","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1002\/jmri.23675","article-title":"Correlation of volume transfer coefficient Ktrans with histopathologic grades of gliomas","volume":"36","author":"Zhang","year":"2012","journal-title":"J Magn Reson Imaging"},{"key":"2024050215132059800_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pnmrs.2013.04.002","article-title":"Arterial input function in perfusion MRI: a comprehensive review","volume":"74","author":"Calamante","year":"2013","journal-title":"Prog Nucl Magn Reson Spectrosc"},{"key":"2024050215132059800_b9","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1111\/jon.12254","article-title":"Dynamic contrast-enhanced perfusion MRI of high grade brain gliomas obtained with arterial or venous waveform input function","volume":"26","author":"Filice","year":"2016","journal-title":"J Neuroimaging"},{"key":"2024050215132059800_b10","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.mri.2017.09.010","article-title":"Modification of population based arterial input function to incorporate individual variation","volume":"45","author":"Kim","year":"2018","journal-title":"Magn Reson Imaging"},{"key":"2024050215132059800_b11","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1088\/1361-6560\/aa553c","article-title":"Improved hepatic arterial fraction estimation using cardiac output correction of arterial input functions for liver DCE MRI","volume":"62","author":"Chouhan","year":"2017","journal-title":"Phys Med Biol"},{"key":"2024050215132059800_b12","article-title":"Dynamic contrast-enhanced MRI in renal tumors: common subtype differentiation using pharmacokinetics","volume":"7","author":"Wang","year":"2017","journal-title":"Sci Rep"},{"key":"2024050215132059800_b13","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1002\/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S","article-title":"Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols","volume":"10","author":"Tofts","year":"1999","journal-title":"J Magn Reson Imaging"},{"key":"2024050215132059800_b14","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J Chiropr Med"},{"key":"2024050215132059800_b15","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.mri.2017.04.006","article-title":"Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI","volume":"40","author":"Keil","year":"2017","journal-title":"Magn Reson Imaging"},{"key":"2024050215132059800_b16","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.3174\/ajnr.A3825","article-title":"Glioma: application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading","volume":"35","author":"Jung","year":"2014","journal-title":"AJNR Am J Neuroradiol"}],"container-title":["The British Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/94\/1119\/20200699\/57374123\/bjr.20200699.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/94\/1119\/20200699\/57374123\/bjr.20200699.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T15:35:36Z","timestamp":1714664136000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bjr\/article\/doi\/10.1259\/bjr.20200699\/7451670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,17]]},"references-count":16,"journal-issue":{"issue":"1119","published-online":{"date-parts":[[2020,12,17]]},"published-print":{"date-parts":[[2021,3,1]]}},"URL":"https:\/\/doi.org\/10.1259\/bjr.20200699","relation":{},"ISSN":["0007-1285","1748-880X"],"issn-type":[{"type":"print","value":"0007-1285"},{"type":"electronic","value":"1748-880X"}],"subject":[],"published-other":{"date-parts":[[2021,3,1]]},"published":{"date-parts":[[2020,12,17]]},"article-number":"20200699"}}