P04.17 Differential diagnosis of gliomas using Digital Holographic Microscopy

• Published in: Neuro-oncology (Charlottesville, Va.) Va.), 2019-09, Vol.21 (Supplement_3), p.iii32-iii33
• Main Authors: Calin, V L, Petrescu, G E D, Mihailescu, M, Brehar, F M, Lisievici, M, Comanescu, B, Tarba, N, Gorgan, M R, Savopol, T, Moisescu, M G
• Format: Article
• Language: English
• Subjects: Poster Presentations
• ISSN: 1522-8517; 1523-5866
• DOI: 10.1093/neuonc/noz126.112

Abstract BACKGROUND The clinical course and prognostic of gliomas depend on the tumor histological and molecular features. The histopathological diagnosis requests well-trained specialists and multi-step operational procedures for sample preparation. Faster and more objective protocols should be implemented in support of pathologists. The Quantitative Phase Imaging based methods are biologically proved to be efficient in revealing, without any labeling, important characteristics of the living specimens having different structural complexity. We used Digital Holographic Microscopy (DHM) to acquire QPIs and to analyze glioma samples in order to discriminate glioma tissues of various malignancy grades. MATERIAL AND METHODS Grade II glioma (GM) and grade IV glioblastoma (GBM) tissues were collected from patients who underwent surgery. For each sample, two consecutive slices were fixed with formalin, embedded in paraffin and cut at 4 µm thickness. One slice was stained using hematoxylin and eosin (H&E) and the other slice was left unstained. The pathologist diagnosed H&E slides as GM or GBM and the corresponding unstained slides were accordingly labeled. Holograms of unstained sections were acquired using a LyncéeTec DHM®-R1000 digital holographic microscope (at 664.5 nm). QPIs were reconstructed using the Koala dedicated software, and then the distribution of the phase shift values in the image was characterized by various statistical parameters (mean, variance, kurtosis, skewness, energy, entropy). RESULTS A total of 78 images were analyzed, 33 for grade II gliomas and 45 for grade IV glioblastomas, the areas being randomly selected, as the tissue is highly homogeneous. Lower values of Mean, Variance and Energy and higher values of Kurtosis and Entropy were found for GM compared to GBM (Mann-Whitney test was performed for proofing the statistically significance). No statistical difference was observed for Skewness. As the thickness of the samples was constant, variations of these parameters may be attributed to different distributions of the refractive index within the samples, which in turn is directly related to the protein content and structural features of the tissue. CONCLUSION The analyze of unstained biopsies of glioma tumors based on DHM could be used for faster and more accurate diagnosis, offering efficient optical markers to distinguish between levels of malignancy with high statistical confidence. Our findings can be further exploited for automati