Rapid Glioma Subtype Classification Using Label-Free Terahertz Time-Domain Spectroscopy and Hierarchical Machine Learning.

Authors

Peiyuan Sun, Minghui Du, Zhiyan Sun, Xianhao Wu, Rui Tao, Tianyi Bi, Yubo Wu, Zhaohui Zhang, Tianyao Zhang, Xiaoyan Zhao, Dabiao Zhou, Pei Yang

Published in

Journal of biophotonics. Volume 19. Issue 9. Pages e70354.

Abstract

Rapid, label-free characterization of glioma tissue remains challenging in time-sensitive clinical workflows. This exploratory study evaluated whether terahertz time-domain spectroscopy (THz-TDS)-derived spectral and dielectric features could support classification of major adult-type 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-TDS workflow. For each slice, 492 features were extracted from six dielectric-parameter families across 82 frequency points from 0.2 to 1.4 THz. To reduce slice-level information leakage, model development and validation used a strict patient-wise split, with 50 training patients and 13 held-out validation patients. A hierarchical framework combining LASSO-based feature selection, principal component analysis, and random forest classification was constructed. In validation, the final framework achieved a slice-level accuracy of 0.818 and a macro-F1 score of 0.760, supporting further investigation of THz-TDS for rapid ex vivo glioma tissue characterization.

PMID:
42734121
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

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