Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy

Aug 5, 2026

Raman spectroscopy (RS) supports neurooncological diagnostics from intraoperative tumor detection to molecular characterization of tissue specimens. This study applies RS to monitor vibrational signatures of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms, addressing practical challenges within routine neuropathology workflows. A dataset of 82 tumors (679 measurements) is used to build machine-learning classifiers with training and external validation cohorts, evaluated via AUROC and AUPR. Random forest models differentiate glioma types and identify the primary origin of brain metastases, while RS enables tumor typing from necrotic biopsy fragments beyond conventional light microscopy. Validation highlights data complexity driven by tissue processing and residual fixation/paraffin components, defining both RS potential and limitations for diagnostic use.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Materials and Methods

Raman spectroscopy on FFPE tissue

Machine learning framework and validation cohorts

Performance metrics

Results

Classification of glioma types

Identification of brain metastasis origin

Raman band analysis and misclassification handling

Discussion

Sources of spectral complexity in FFPE preparations

Clinical implications for neuropathology workflows

Conclusion

brain sciences Article Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy Gilbert Georg Klamminger 1,2,3, *,†, Laurent Mombaerts 4,†, Françoise Kemp 4, Finn Jelke 5,6, Karoline Klein 5,7, Rédouane Slimani 6,8, Giulia Mirizzi 5,7, Andreas Husch 5,9, Frank Hertel 5,7, Michel Mittelbronn 4,8,9,10,11 and Felix B. Kleine Borgmann 3,5,8,12, * Citation: Klamminger, G.G.; Mombaerts, L.; Kemp, F.; Jelke, F.; Klein, K.; Slimani, R.; Mirizzi, G.; Husch, A.; Hertel, F.; Mittelbronn, M.; et al. Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy. Brain Sci. 2024, 14, 301 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)brainsci14040301 Academic Editors: Juan Manuel Gorriz, Stavros I. Dimitriadis, Christian Salvatore and Petronilla Battista Received: 26 February 2024 Revised: 15 March 2024 Accepted: 22 March 2024 Published: 23 March 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Department of General and Special Pathology, Saarland University (USAAR), 66424 Homburg, Germany 2 Department of General and Special Pathology, Saarland University Medical Center (UKS), 66424 Homburg, Germany 3 National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg 4 Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg 5 National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg 6 Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg 7 Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany 8 Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg 9 Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg 10 Department of Life Sciences and Medicine (DLSM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg 11 Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg 12 Hôpitaux Robert Schuman, 1130 Luxembourg, Luxembourg * Correspondence: [gilbert-georg.klamminger@uks.eu](gilbert-georg.klamminger@uks.eu) (G.G.K.); [felix.kleineborgmann@lih.lu](felix.kleineborgmann@lih.lu) (F.B.K.B.); Tel.: +49-6841-16-23867 (G.G.K.); +352-26970-898 (F.B.K.B.)† These authors contributed equally to this work. Abstract: Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri-and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF 2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover,

FAQs

How was Raman spectroscopy used for paraffin-embedded brain tumor analysis?

Raman spectroscopy measured molecular vibrational characteristics in formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms, including primary tumors, meningiomas, and brain metastases, to capture spectroscopic signatures relevant for classification.

What machine learning approach was applied and how was it evaluated?

The study trained random forest machine learning algorithms using training cohorts and external validation cohorts, assessing performance with common metrics such as AUROC and AUPR.

What key challenge in FFPE Raman data was highlighted during validation?

Validation showed considerable complexity in spectroscopic data, attributed not only to biological tissue effects after chemical processing but also to residual components from fixation and paraffin-embedding.

Aug 5, 2026

Upload by Emma Wilson