D33.0
Conditions
Interventions
Group 1: Monocentric prospective clinical observational study. In patients with suspected primary or recurrent convexity meningioma and interdisciplinary indication for Simpson grade I resection, a sm
Sponsors
Universtitätsklinik Köln
Eligibility
Sex/Gender
All
Age
18 Years to 100 Years
Inclusion criteria
Inclusion criteria: Imaging suspicion of primary convexity meningioma and/or recurrent convexity meningioma Interdisciplinary neuro-oncological tumor board recommendation for resection with Simpson grade I intention (complete removal including dural attachment and involved bone) Patient has capacity to consent Patient is undergoing tumor surgery at the neuro-oncological center of the University Hospital of Cologne independent of the study Written informed consent for study participation
Exclusion criteria
Exclusion criteria: Absence of consent for study participation Patient lacks capacity to consent
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| WHAT: (1) Tumor detection – identification of tumor cells in areas considered tumor-free after Simpson grade I resection by the surgeon, using conventional neuropathology. (2) Technical feasibility – demonstration of SRH image processing of dural samples in various conditions using the NIO Laser Imaging System. (3) Diagnostic accuracy of the AI-based analysis algorithm – assessment of its ability to detect microscopic tumor infiltration in the dura compared to final conventional neuropathology (sensitivity, specificity, accuracy). (4) Spatial correlation – analysis of spatial concordance between microscopic tumor residue detection and tumor-free dural tail samples based on pre- and postoperative MRI data. WHEN: Intraoperatively (SRH imaging within max. 10 hours after tissue removal), postoperatively (conventional neuropathological workup up to 15 days after surgery), and after completion of AI model development during the validation phase. HOW: SRH imaging via NIO Laser Imaging System, conventional neuropathological diagnostics (histomorphology, SSTR2 immunohistochemistry, molecular genetics), AI-based image classification (CNN architectures), correlation with pre-/postoperative MRI imaging. | — |
Secondary
| Measure | Time frame |
|---|---|
| WHAT: Histological characterization of the thickened reactive dural tail – comprehensive histological analysis and depiction of structural properties of the thickened, reactively altered dura in the margin and transitional zone using the NIO Laser Imaging System and the AI-based analysis algorithm. WHEN: After completion of neuropathological workup and SRH imaging during the data analysis phase. HOW: Comparative analysis of SRH images and conventional histopathological findings regarding structural features of reactively altered dura; evaluation of the AI algorithm's ability to differentiate reactive changes. | — |
Countries
Germany
Contacts
Public ContactVolker Neuschmelting
Uniklinik Köln
Outcome results
None listed