The fluorescent tracer 5-aminolevulinc acid was introduced to visualize brain tumors intraoperatively, but suffers from drawbacks such as limited sensitivity for certain tumor types. Optical coherence tomography (OCT) is a non-invasive imaging modality, which has recently found its application in neuroscience by contributing label-free tissue information. We present one of the first radiomics-based analyses to capture the form and texture of glioma samples resected during fluorescence-guided surgery in a large cohort of multimodal OCT-based microscopy (OCM) imaging data. Concluding, we report encouraging results for the prediction of tumor infiltration, entity and molecular biomarkers with accuracies as high as 96%.
Maximal safe resection is key to prolonging overall survival during intracranial surgery. Capabilities of optical coherence tomography (OCT) to discriminate tissue types have been previously reported. We utilized an OCT-based microscope (OCM) to acquire reflectivity data of resected brain tumor samples and perform textural analyses. Twelve features showed statistically significant differences between different tissue types. Three of those – contrast, energy and entropy – are described in detail in this work. Contrast and entropy increased with tissue malignancy, and energy decreased. These results suggest that OCM data-based textural analyses are capable of differentiating different tissue types based on their intrinsic structures.
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