In:
Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii149-vii149
Abstract:
Raman spectroscopy (RS) has shown its applicability in neurooncological diagnostics ranging from intraoperative tumor identification to peri- and postoperative tissue analyses. In the present study, we applied RS to track changes in the molecular vibrational status of a broad spectrum of formalin fixed paraffin-embedded (FFPE) intracranial neoplasms (primary brain tumors, meningiomas, brain metastases) and evaluated its potential as an additional method in the neuropathology toolbox, considering specific challenges when employing RS on FFPE tissue. Material and METHODS We examined 82 cases of intracranial neoplasms (679 individual measurements) by RS and applied a machine learning pipeline for recognition of spectral properties. The discrimination potential of the machine learning algorithms was evaluated using standard performance metrics such as AUROC and AUPR values, macro and weighted average of accuracy, precision, recall, and f1 scores. To address occurring misclassifications and further evaluate our models we searched for important Raman bands usable for tumor identification. RESULTS Using our trained machine learning model, we differentiated between different types of gliomas and determined the primary origin in case of a brain metastasis. We further spectroscopically diagnosed tumor types solely based on biopsy fragments of necrosis, something not possible by means of light microscopy. During the validation process we confirmed a high complexity within the spectroscopic data, possibly resulting not only from biological tissue which has undergone a rough chemical procedure but also from residual components of the fixation/paraffination process. CONCLUSIONS Our study demonstrates possibilities and limits of RS as a potential diagnostic tool in neuropathology, considering accompanying difficulties in the vibrational spectroscopic examination of FFPE tissue.
Type of Medium:
Online Resource
ISSN:
1522-8517
,
1523-5866
DOI:
10.1093/neuonc/noac209.574
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2022
detail.hit.zdb_id:
2094060-9
