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Performance metrics (accuracy, sensitivity, F1-score) were derived for each task, alongside feature selection frequencies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The uni-sequence machine learning models using FLAIR imaging features excelled for WHO CNS grade classification (57.4% accuracy), ASL for final pathological diagnosis (81.5% accuracy), and T1w for IDH status classification (66.7% accuracy). Interestingly, the combination of all sequences did not lead to significant improvements compared to the best performing uni-sequence machine learning models for the three tasks. Regardless of the input sequence, the models consistently relied on similar features, such as shape and demographic variables (\n                      <jats:italic>e.g.,<\/jats:italic>\n                      patient age and tumor sphericity).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Single sequences, such as FLAIR, ASL, and T1w, achieved the highest individual accuracies in the radiomics models for classification of tumor grade, final diagnosis, and IDH status, and may inform the design of more streamlined imaging protocols.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11547-026-02218-9","type":"journal-article","created":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T11:29:21Z","timestamp":1788694161000},"page":"1877-1885","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing MRI sequence selection for glioma classification: a radiomics-based analysis of WHO CNS grade, final pathological diagnosis, and IDH status"],"prefix":"10.1007","volume":"131","author":[{"given":"Kimberly","family":"Amador","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helge","family":"Kniep","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Fiehler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nils Daniel","family":"Forkert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4408-2429","authenticated-orcid":false,"given":"Thomas","family":"Lindner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,6]]},"reference":[{"issue":"7","key":"2218_CR1","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1093\/neuonc\/nou087","volume":"16","author":"QT Ostrom","year":"2014","unstructured":"Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher JL, Langer CE, Pekmezci M, Schwartzbaum JA, Turner MC, Walsh KM, Wrensch MR, Barnholtz-Sloan JS (2014) The epidemiology of glioma in adults: a \u201cstate of the science\u201d review. 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