%0 Journal Article
%A Scheffler, Pierre
%A Straehle, Jakob
%A El Rahal, Amir
%A Erny, Daniel
%A Mizaikoff, Boris
%A Vasilikos, Ioannis
%A Prinz, Marco
%A Coenen, Volker A.
%A Kühn, Julia
%A Scherer, Florian
%A Heiland, Dieter Henrik
%A Schnell, Oliver
%A Roelz, Roland
%A Beck, Jürgen
%A Reinacher, Peter C.
%A Neidert, Nicolas
%T Intraoperative classification of CNS lymphoma and glioblastoma by AI-based analysis of Stimulated Raman Histology (SRH)
%J Brain and spine
%V 5
%@ 2772-5294
%C [Amsterdam]
%I Elsevier B.V.
%M DKFZ-2025-00349
%P 104187
%D 2025
%X Introduction: Early diagnosis is important to differentiate central nervous system lymphomas (CNSL) from themain differential diagnosis, glioblastoma (GBM), because of different primary treatment modalities for theseentities. Due to neurological deficits, diagnostic stereotactic biopsies often need to be performed urgently. In thissetting the availability of an intraoperative neuropathological assessment is limited.Research question: This study uses AI-based analysis of Stimulated Raman Histology (SRH) to establish a classifierdistinguishing CNSL from glioblastoma in an intraoperative setting.Material and methods: We collected 126 intraoperative SRH images from 40 patients diagnosed with CNSL. TheseSRH images were divided into patches, measuring 224 x 224 pixels each. Additionally, we used a comparativedataset of 87 SRH images from 31 patients with GBM as a control group to train and validate a neural networkbased on the CTransPath architecture. Two distinct diagnostic categories were established: “Lymphoma” and“Glioblastoma".Results: Our model demonstrated an accuracy rate of 92.5
%F PUB:(DE-HGF)16
%9 Journal Article
%R 10.1016/j.bas.2025.104187
%U https://inrepo02.dkfz.de/record/298913