{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T02:36:20Z","timestamp":1790908580476,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2002,12,31]],"date-time":"2002-12-31T00:00:00Z","timestamp":1041292800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["IST-1999-10310"],"award-info":[{"award-number":["IST-1999-10310"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Royal Society, British Council Concerted Action","award":["HB97-0139"],"award-info":[{"award-number":["HB97-0139"]}]},{"name":"CICYT","award":["SAF99-0101"],"award-info":[{"award-number":["SAF99-0101"]}]},{"name":"Cancer Research U.K., formerly known as the Cancer Research Campaign","award":["SP1971\/0405"],"award-info":[{"award-number":["SP1971\/0405"]}]},{"DOI":"10.13039\/501100002809","name":"Generalitat de Catalunya","doi-asserted-by":"crossref","award":["CIRIT XT-2000-43"],"award-info":[{"award-number":["CIRIT XT-2000-43"]}],"id":[{"id":"10.13039\/501100002809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002809","name":"Generalitat de Catalunya","doi-asserted-by":"crossref","award":["CIRIT 2001 SGR-194"],"award-info":[{"award-number":["CIRIT 2001 SGR-194"]}],"id":[{"id":"10.13039\/501100002809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2003,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Automated pattern recognition techniques are needed to help radiologists categorize MRS data of brain tumors according to histological type and grade. A major question is whether a computer program \u201ctrained\u201d on spectra from one hospital will be able to classify those from another, particularly if the acquisition protocol is different. A subset of 144 histopathologically validated brain tumor spectra in the INTERPRET database, obtained from three of the collaborating centers, was grouped into meningiomas, low\u2010grade astrocytomas, and \u201caggressive tumors\u201d (glioblastomas and metastases). Spectra from two centers formed the training set (94 spectra) while the third acted as the test set (50 spectra). Linear discriminant analysis successfully classified 48\/50 in the test set; the remaining two were atypical cases. When the training and test sets were combined, 133 of the 144 spectra were correctly classified using the leave\u2010one\u2010out procedure. These spectra had been obtained using different sequences (STEAM and PRESS), different echo times (20, 30, 31, and 32 ms), different repetition times (1600 and 2000 ms), and different manufacturers' instruments (GE and Philips). Pattern recognition algorithms are less sensitive to acquisition parameters than had been expected. Magn Reson Med 49:29\u201336, 2003. \u00a9 2003 Wiley\u2010Liss, Inc.<\/jats:p>","DOI":"10.1002\/mrm.10315","type":"journal-article","created":{"date-parts":[[2003,1,1]],"date-time":"2003-01-01T16:01:57Z","timestamp":1041436917000},"page":"29-36","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":148,"title":["Automated classification of short echo time in in vivo\n                    <sup>1<\/sup>\n                    H brain tumor spectra: A multicenter study"],"prefix":"10.1002","volume":"49","author":[{"given":"A. Rosemary","family":"Tate","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carles","family":"Maj\u00f3s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angel","family":"Moreno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franklyn A.","family":"Howe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John R.","family":"Griffiths","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carles","family":"Ar\u00fas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2002,12,31]]},"reference":[{"key":"e_1_2_6_2_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781482270105"},{"key":"e_1_2_6_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s002340000427"},{"key":"e_1_2_6_4_2","first-page":"561","volume-title":"A prototype decision support system for MR spectroscopy\u2010assisted diagnosis of brain tumors","author":"Underwood J","year":"2001"},{"key":"e_1_2_6_5_2","unstructured":"TateAR Julia\u2010SapeM LadroueC MurphyM LoosemoreA BellB WilkinsP CapdevilaA MajosC MorenoA HoweFA Ar\u00fasC GriffithsJR.Automated classification of brain tumors from1H MRS spectra in INTERPRET a multi\u2010center collaboration. In: Proc 10th Annual Meeting ISMRM Hawaii 2002. p572."},{"key":"e_1_2_6_6_2","unstructured":"TateAR GriffithsJR HoweFA MorenoA BarbaI PujolJ Ar\u00fasC.Differentiating types of human brain tumors by MRS. A comparison of pre\u2010processing methods and echo times. 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A users's guide to the magnetic resonance user interface software package","author":"van den Boogaart A","year":"1997"},{"key":"e_1_2_6_20_2","unstructured":"TateAR LadroueC.Developing classifiers for single\u2010voxel1H brain tumor in vivo spectra for the INTERPRET decision support tool. 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In: Proc European Symposium on Artificial Neural Networks ESANN 2002. 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