P04.07.a Ai-based Nuclear Morphometry Reveals Distinct Morphological Patterns Between Idh-mutant, Idh-wildtype, and Non-tumorous Cells in Stimulated Raman Histology
Year: 2025
Type: conference-abstract
Abstract: BACKGROUND Isocitrate dehydrogenase (IDH) mutation status significantly influences diffuse glioma classification. While molecular testing remains the gold standard, we investigated whether cell nuclear morphology in virtual H&E-like images of diffuse gliomas and non-tumorous tissues represents a critical feature category that could serve as a foundation for AI-based single-cell molecular subtyping using label-free Stimulated Raman Histology (SRH). MATERIAL AND METHODS Label-free and unprocessed tissue specimens were intraoperatively imaged using a portable SRH imager. We included 127 patients from a previous multicenter testing cohort, yielding 131 SRH images containing 539,145 cell regions. Specimens comprised IDH-mutant Astrocytoma (Grade 2-4), IDH-mutant Oligodendroglioma with 1p/19q-codeletion (Grade 2-3), and IDH-wildtype Glioblastoma (Grade 4). Normal tissue came from healthy brain and gliosis specimens. Cell nuclei were detected and segmented using a Mask R-CNN with a ResNet-50 backbone trained on 550 SRH-annotated patches from normal brain tissue and six different brain tumor types. We extracted three morphological features (area, perimeter, eccentricity) and analyzed their significance across cell, patch, and slide-level. RESULTS Analysis revealed significant morphological differences between IDH-mutation classes (p<0.0001). IDH-mutant cells showed larger nuclear areas (60.62±33.63μm²) and perimeters (30.21±8.92μm) versus IDH-wildtype (51.47±28.23μm², 28.27±8.48μm) and normal cells (46.67±27.69μm², 26.57±8.29μm). Eccentricity also differed significantly (IDH-mutant: 0.71±0.16, IDH-wildtype: 0.74±0.15, normal: 0.69±0.16). These differences persisted at patch and slide levels, with statistical significance by both parametric and non-parametric testing. CONCLUSION These distinctive morphological features help explain the high accuracy of current AI-based molecular SRH classifiers and demonstrate potential for single-cell molecular classification of diffuse gliomas. This capability could allow fine-granular discrimination between IDH-mutant tumor cells, IDH-wildtype tumor cells, and normal brain cells at glioma margins, potentially redefining AI-guided resection beyond current visual assessment limitations and time constraints.
Source: Neuro-Oncology
Authors: D Reinecke, N Goff, J Markert, N Maarouf, A Smith, A Bhattacharya, T Hollon, E Landgraf, C Jiang, A Chowdury, X Hou, S Camelo-Piragua, A Meissner, G Fürtjes, N M Müller, A Al-Shughri, L Koerner, T Roetzer-Pejrimovsky, G Widhalm, S Hervey-Jumper, C Freudiger, R Goldbrunner, J G Golfinos, V Neuschmelting, D Orringer
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4414799791