{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:14:44Z","timestamp":1772165684809,"version":"3.50.1"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T00:00:00Z","timestamp":1579132800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T00:00:00Z","timestamp":1579132800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2015CB755500"],"award-info":[{"award-number":["2015CB755500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2012B031800102"],"award-info":[{"award-number":["2012B031800102"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2016A020213004"],"award-info":[{"award-number":["2016A020213004"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004000","name":"Guangzhou Science and Technology Program key projects","doi-asserted-by":"publisher","award":["201803010056"],"award-info":[{"award-number":["201803010056"]}],"id":[{"id":"10.13039\/501100004000","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81572500"],"award-info":[{"award-number":["81572500"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Young Talents","award":["2016RS3036"],"award-info":[{"award-number":["2016RS3036"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Differentiating glioma recurrence from treatment-related changes can be challenging on conventional imaging. We evaluated the efficacy of quantitative parameters measured by dual-energy spectral computed tomographic (CT) for this differentiation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Twenty-eight patients were examined by dual-energy spectral CT. The effective and normalized atomic number (Z\n                      <jats:sub>eff<\/jats:sub>\n                      and Z\n                      <jats:sub>eff-N,<\/jats:sub>\n                      respectively); spectral Hounsfield unit curve (\u03bb\n                      <jats:sub>HU<\/jats:sub>\n                      ) slope; and iodine and normalized iodine concentration (IC and IC\n                      <jats:sub>N<\/jats:sub>\n                      , respectively) in the post-treatment enhanced areas were calculated. Pathological results or clinicoradiologic follow-up of \u22652\u2009months were used for final diagnosis. Nonparametric and\n                      <jats:italic>t<\/jats:italic>\n                      -tests were used to compare quantitative parameters between glioma recurrence and treatment-related changes. Sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively), and accuracy were calculated using receiver operating characteristic (ROC) curves. Predictive probabilities were used to generate ROC curves to determine the diagnostic value.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Examination of pre-contrast \u03bb\n                      <jats:sub>HU<\/jats:sub>\n                      , Z\n                      <jats:sub>eff<\/jats:sub>\n                      , Z\n                      <jats:sub>eff-N<\/jats:sub>\n                      , IC, IC\n                      <jats:sub>N<\/jats:sub>\n                      , and venous phase IC\n                      <jats:sub>N<\/jats:sub>\n                      showed no significant differences in quantitative parameters (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&gt;\u20090.05). Venous phase \u03bb\n                      <jats:sub>HU<\/jats:sub>\n                      , Z\n                      <jats:sub>eff<\/jats:sub>\n                      , Z\n                      <jats:sub>eff-N<\/jats:sub>\n                      , and IC in glioma recurrence were higher than in treatment-related changes (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). The optimal venous phase threshold was 1.03, 7.75, 1.04, and 2.85\u2009mg\/cm\n                      <jats:sup>3<\/jats:sup>\n                      , achieving 66.7, 91.7, 83.3, and 91.7% sensitivity; 100.0, 77.8, 88.9, and 77.8% specificity; 100.0, 73.3, 83.3, and 73.3% PPV; 81.8, 93.3, 88.9, and 93.3% NPV; and 86.7, 83.3, 86.7, and 83.3% accuracy, respectively. The respective areas under the curve (AUCs) were 0.912, 0.912, 0.931, and 0.910 in glioma recurrence and treatment-related changes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Glioma recurrence could be potentially differentiated from treatment-related changes based on quantitative values measured by dual-energy spectral CT imaging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-019-0406-5","type":"journal-article","created":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T08:04:49Z","timestamp":1579161889000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Dual-energy spectral CT quantitative parameters for the differentiation of Glioma recurrence from treatment-related changes: a preliminary study"],"prefix":"10.1186","volume":"20","author":[{"given":"Yanchun","family":"Lv","sequence":"first","affiliation":[]},{"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xiaofei","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Li","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Haoqiang","family":"He","sequence":"additional","affiliation":[]},{"given":"Zhigang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Lujun","family":"Han","sequence":"additional","affiliation":[]},{"given":"Meili","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yadi","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Chengcheng","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Rong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chuanmiao","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Yinsheng","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6868-9866","authenticated-orcid":false,"given":"Zhongping","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,16]]},"reference":[{"key":"406_CR1","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/S1470-2045(08)70125-6","volume":"9","author":"D Brandsma","year":"2008","unstructured":"Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 2008;9:453\u201361.","journal-title":"Lancet Oncol"},{"key":"406_CR2","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1016\/j.ejrad.2014.09.018","volume":"83","author":"H Zhang","year":"2014","unstructured":"Zhang H, Ma L, Wang Q, Zheng X, Wu C, Xu B. Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: a systematic review and meta-analysis. Eur J Radiol. 2014;83:2181\u20139.","journal-title":"Eur J Radiol"},{"key":"406_CR3","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1093\/neuonc\/nos307","volume":"15","author":"N Verma","year":"2013","unstructured":"Verma N, Cowperthwaite MC, Burnett MG, Markey MK. Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro-Oncology. 2013;15:515\u201334.","journal-title":"Neuro-Oncology"},{"key":"406_CR4","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.ijrobp.2013.05.015","volume":"87","author":"ST Chao","year":"2013","unstructured":"Chao ST, Ahluwalia MS, Barnett GH, Stevens GHJ, Murphy ES, Stockham AL, et al. Challenges with the diagnosis and treatment of cerebral radiation necrosis. Int J Radiat Oncol Biol Phys. 2013;87:449\u201357.","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"406_CR5","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1097\/RLU.0b013e318262c76a","volume":"37","author":"MS Enslow","year":"2012","unstructured":"Enslow MS, Zollinger LV, Morton KA, Kadrmas DJ, Butterfield RI, Christian PE, et al. Comparison of 18F-fluorodeoxyglucose and 18F-fluorothymidine PET in differentiating radiation necrosis from recurrent glioma. Clin Nucl Med. 2012;37:854\u201361.","journal-title":"Clin Nucl Med"},{"key":"406_CR6","doi-asserted-by":"publisher","first-page":"1924","DOI":"10.3174\/ajnr.A3980","volume":"35","author":"T Pyka","year":"2014","unstructured":"Pyka T, Gempt J, Ringel F, H\u00fcttinger S, van Marwick S, Nekolla S, et al. Prediction of glioma recurrence using dynamic 18F-fluoroethyltyrosine PET. AJNR Am J Neuroradiol. 2014;35:1924\u20139.","journal-title":"AJNR Am J Neuroradiol"},{"key":"406_CR7","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1148\/rg.325125002","volume":"32","author":"R Shah","year":"2012","unstructured":"Shah R, Vattoth S, Jacob R, Manzil FF, O'Malley JP, Borghei P, et al. Radiation necrosis in the brain imaging features and differentiation from tumor recurrence. Radiographics. 2012;32:1343\u201359.","journal-title":"Radiographics."},{"key":"406_CR8","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1148\/radiol.14132868","volume":"273","author":"HS Kim","year":"2014","unstructured":"Kim HS, Goh MJ, Kim N, Choi CG, Kim SJ, Kim JH. Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology. 2014;273:831\u201343.","journal-title":"Radiology."},{"key":"406_CR9","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1148\/radiol.11101425","volume":"259","author":"P Lv","year":"2011","unstructured":"Lv P, Lin XZ, Li J, Li W, Chen K. Differentiation of small hepatic hemangioma from small hepatocellular carcinoma: recently introduced spectral CT method. Radiology. 2011;259:720\u20139.","journal-title":"Radiology."},{"key":"406_CR10","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1097\/RLI.0b013e318229fef3","volume":"47","author":"M Li","year":"2012","unstructured":"Li M, Zheng X, Li J, Yang Y, Lu C, Xu H, et al. Dual-energy computed tomography imaging of thyroid nodule specimens comparison with pathologic findings. Investig Radiol. 2012;47:58\u201364.","journal-title":"Investig Radiol"},{"key":"406_CR11","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1016\/j.acra.2013.02.011","volume":"20","author":"XF Zhang","year":"2013","unstructured":"Zhang XF, Lu Q, Wu LM, Zou AH, Hua XL, Xu JR. Quantitative iodine-based material decomposition images with spectral CT imaging for differentiating prostatic carcinoma from benign prostatic hyperplasia. Acad Radiol. 2013;20:947\u201356.","journal-title":"Acad Radiol"},{"key":"406_CR12","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.1007\/s00330-012-2747-0","volume":"23","author":"Y Yu","year":"2013","unstructured":"Yu Y, Lin X, Chen K, Chai W, Hu S, Tang R, et al. Hepatocellular carcinoma and focal nodular hyperplasia of the liver: differentiation with CT spectral imaging. Eur Radiol. 2013;23:1660\u20138.","journal-title":"Eur Radiol"},{"key":"406_CR13","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1148\/radiol.14140481","volume":"275","author":"X Liu","year":"2015","unstructured":"Liu X, Ouyang D, Li H, Zhang R, Lv Y, Yang A, et al. Papillary thyroid cancer: dual-energy spectral CT quantitative parameters for preoperative diagnosis of metastasis to the cervical lymph nodes. Radiology. 2015;275:167\u201376.","journal-title":"Radiology."},{"key":"406_CR14","doi-asserted-by":"publisher","first-page":"899","DOI":"10.3174\/ajnr.A5124","volume":"38","author":"A Jena","year":"2017","unstructured":"Jena A, Taneja S, Jha A, Damesha NK, Negi P, Jadhav GK, et al. Multiparametric evaluation in differentiating glioma recurrence from treatment-induced necrosis using simultaneous 18F-FDG-PET\/MRI: a single-institution retrospective study. AJNR Am J Neuroradiol. 2017;38:899\u2013907.","journal-title":"AJNR Am J Neuroradiol"},{"key":"406_CR15","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1097\/RCT.0b013e3182976365","volume":"37","author":"A Srinivasan","year":"2013","unstructured":"Srinivasan A, Parker RA, Manjunathan A, Ibrahim M, Shah GV, Mukherji SK. Differentiation of benign and malignant neck pathologies preliminary experience using spectral computed tomography. J Comput Assist Tomogr. 2013;37:666\u201372.","journal-title":"J Comput Assist Tomogr"},{"key":"406_CR16","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1016\/j.ijrobp.2012.09.027","volume":"85","author":"EJ Moding","year":"2013","unstructured":"Moding EJ, Clark DP, Qi Y, Li Y, Ma Y, Ghaghada K, et al. Dual-energy micro-computed tomography imaging of radiation-induced vascular changes in primary mouse sarcomas. Int J Radiat Oncol Biol Phys. 2013;85:1353\u20139.","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"406_CR17","doi-asserted-by":"publisher","first-page":"S34","DOI":"10.2214\/AJR.12.9113","volume":"199","author":"TJ Vogl","year":"2012","unstructured":"Vogl TJ, Schulz B, Bauer RW, St\u00f6ver T, Sader R, Tawfik AM. Dual-energy CT applications in head and neck imaging. AJR Am J Roentgenol. 2012;199:S34\u20139.","journal-title":"AJR Am J Roentgenol"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-019-0406-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12880-019-0406-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-019-0406-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T19:16:55Z","timestamp":1610651815000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-019-0406-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,16]]},"references-count":17,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["406"],"URL":"https:\/\/doi.org\/10.1186\/s12880-019-0406-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.2.15990\/v3","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.2.15990\/v2","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.2.15990\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,16]]},"assertion":[{"value":"15 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The written consents were obtained from the patients or the relatives of patients. The study was approved by the Ethics Committee of the Sun Yat-sen University Cancer Center.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All data published here are under the consent for publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"5"}}