TY  - JOUR
AU  - Scheffler, Pierre
AU  - Neidert, Nicolas
AU  - Straehle, Jakob
AU  - Erny, Daniel
AU  - Prinz, Marco
AU  - Hubbe, Ulrich
AU  - Rölz, Roland
AU  - Heiland, Dieter Henrik
AU  - Beck, Jürgen
AU  - El Rahal, Amir
TI  - Artificial intelligence-based analysis and diagnosis of intradural extramedullary spinal tumors by stimulated Raman histology.
JO  - Neuro-oncology advances
VL  - 7
IS  - 1
SN  - 2632-2498
CY  - Oxford
PB  - Oxford University Press
M1  - DKFZ-2026-00017
SP  - vdaf211
PY  - 2025
AB  - Intraoperative Stimulated Raman Histology (SRH) has been reported to be fast and accurate in the assessment of neuro-oncological lesions. However, its application to spinal tumors, especially intradural extramedullary tumors (IDEM), remains underexplored. IDEM primarily include meningiomas and schwannomas, as well as less common entities such as metastases or ependymomas. Given that surgical resection is the primary treatment modality, rapid, artificial intelligence (AI)-driven intraoperative tumor classification based on SRH could enhance surgical decision-making and subsequent management.We acquired 232 SRH images from patients with IDEM using the NIO Laser Imaging System (Invenio Imaging Inc.). We categorized images into three diagnostic classes: 'Meningioma,' 'Schwannoma,' and 'Other.' Images were divided into 224 × 224 pixel patches and used to train and test AI-based image classifiers employing CTransPath, ResNet, and Vision Transformer architectures.Our best-performing model, utilizing the CTransPath architecture, achieved a classification accuracy of 94.3
KW  - artificial intelligence (Other)
KW  - intradural extramedullary tumors (Other)
KW  - stimulated raman histology (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:41473747
C2  - pmc:PMC12746601
DO  - DOI:10.1093/noajnl/vdaf211
UR  - https://inrepo02.dkfz.de/record/307512
ER  -