ABSTRACT
Gliomas are the most common type of cerebral tumors; they occur with increasing incidence in the last decade and have a high rate of mortality. For efficient treatment, fast accurate diagnostic and grading of tumors are imperative. Presently, the grading of tumors is established by histopathological evaluation, which is a time-consuming procedure and relies on the pathologists' experience. Here we propose a supervised machine learning procedure for tumor grading which uses quantitative phase images of unstained tissue samples acquired by digital holographic microscopy. The algorithm is using an extensive set of statistical and texture parameters computed from these images. The procedure has been able to classify six classes of images (normal tissue and five glioma subtypes) and to distinguish between gliomas types from grades II to IV (with the highest sensitivity and specificity for grade II astrocytoma and grade III oligodendroglioma and very good scores in recognizing grade III anaplastic astrocytoma and grade IV glioblastoma). The procedure bolsters clinical diagnostic accuracy, offering a swift and reliable means of tumor characterization and grading, ultimately the enhancing treatment decision-making process.
ABSTRACT
Phase-derived parameters and time autocorrelation functions were used to analyze the behavior of murine B16 cells exposed to different amplitudes of electroporation pulses. Cells were observed using an off-axis digital holographic microscope equipped with a fast camera. Series of quantitative phase images of cells were reconstructed and further processed using MATLAB codes. Projected area, dry mass density, and entropy proved to be predictors for permeabilized cells that swell or collapse. Autocorrelation functions of phase fluctuations in different regions of the cell showed a good correlation with the local effectiveness of permeabilization.
ABSTRACT
The cell refractive index has been proposed as a putative cancer biomarker of great potential, being correlated with cell content and morphology, cell division rate and membrane permeability. We used digital holographic microscopy to compare the refractive index and dry mass density of two B16 murine melanoma sublines of different metastatic potential. Using statistical methods, the distribution of phase shifts within the reconstructed quantitative phase images was analyzed by the method of bimodality coefficients. The observed correlation of refractive index, dry mass density and bimodality profile with the metastatic potential of the cells was validated by real time impedance-based assay and clonogenic tests. We suggest that the refractive index and bimodality analysis of quantitative phase image histograms could be developed as optical biomarkers useful in label-free detection and quantitative evaluation of cell metastatic potential.
ABSTRACT
Changes in optical and shape-related characteristics of B16F10 cells after electroporation were investigated using digital holographic microscopy (DHM). Bipolar rectangular pulses specific for electrochemotherapy were used. Electroporation was performed in an "off-axis" DHM set-up without using exogenous markers. Two types of cell parameters were monitored seconds and minutes after pulse train application: parameters addressing a specifically defined area of the cell (refractive index and cell height) and global cell parameters (projected area, optical phase shift profile and dry mass). The biphasic behavior of cellular parameters was explained by water and mannitol dynamics through the electropermeabilized cell membrane.
