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This exploratory study evaluated whether terahertz time\u2010domain spectroscopy (THz\u2010TDS)\u2010derived spectral and dielectric features could support classification of major adult\u2010type diffuse glioma subtypes, including glioblastoma (GBM), astrocytoma, and oligodendroglioma. A total of 523 glioma tissue slices from 63 patients were analyzed using a unified THz\u2010TDS workflow. For each slice, 492 features were extracted from six dielectric\u2010parameter families across 82 frequency points from 0.2 to 1.4\u2009THz. To reduce slice\u2010level information leakage, model development and validation used a strict patient\u2010wise split, with 50 training patients and 13 held\u2010out validation patients. A hierarchical framework combining LASSO\u2010based feature selection, principal component analysis, and random forest classification was constructed. In validation, the final framework achieved a slice\u2010level accuracy of 0.818 and a macro\u2010F1 score of 0.760, supporting further investigation of THz\u2010TDS for rapid ex\u00a0vivo glioma tissue characterization.<\/jats:p>","DOI":"10.1002\/jbio.70354","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T09:50:13Z","timestamp":1789379413000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rapid Glioma Subtype Classification Using Label\u2010Free Terahertz Time\u2010Domain Spectroscopy and Hierarchical Machine Learning"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2114-9473","authenticated-orcid":false,"given":"Peiyuan","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Neurosurgery Beijing Tiantan Hospital, Capital Medical University  Beijing China"},{"name":"Beijing Neurosurgical Institute, Capital Medical 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