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Using integrated Raman and angular scattering microscopy (IRAM), we follow the response of EMT6 cancer cells to photodynamic therapy (PDT) treatment. The study combines two non-labelling light scattering techniques to extract chemical information and organelle sizes from single cells. Each cell is measured repeatedly over several hours to follow changes in these parameters as the cell responds to the PDT treatment. An automated algorithm identifies which parameters are changing in time. Size parameters extracted from angular scattering measurements show a decrease in the size of 1-micron-diameter scatterers in treated cells. Treated cells also exhibit trends in several Raman peaks, denoting changes in chemical concentrations of proteins, nucleic acids, and lipids. Each of these parameters - acquired from both measurement modalities - can be monitored on a cell-by-cell basis. The ability to track these chemical and structural changes over time allows access to greater knowledge of biological processes.

The tumor extracellular matrix has been focused on by newer approaches to cancer therapy owing to its important functions in the process of drug delivery and cellular metastasis. This study aims to characterize tumor extracellular matrix structures in the presence and absence of therapy, as observed on second harmonic generation (SHG) images through both gray-level co-occurrence matrix (GLCM) derived texture features as well as Minkowski Functionals (MF) that focus on the underlying gray-level topology and geometry of the texture patterns. Thirteen GLCM texture features and three MF texture features were extracted from 119 regions of interest (ROI) annotated on SHG images of treated and control samples of tumor extracellular matrix. These texture features were then used in a machine learning task to classify ROIs as belonging to treated or control samples. A fuzzy k-nearest neighbor classifier was optimized using random sub-sampling cross-validation for each texture feature and the classification performance was calculated on an independent test set using the area under the ROC curve (AUC); AUC distributions of different features were compared using a Mann-Whitney U-test. Two GLCM features f3 and f13 exhibited a significantly higher classification performance when compared to other GLCM features (p < 0.05). The MF feature Area exhibited the best classification performance among the MF features while also being comparable to that obtained with the best GLCM features. These results show that both statistical and topological texture features can be used as quantitative measures is evaluating the effects of therapy on the tumor extracellular matrix.

To date, the lack of light delivery mechanisms to the oral cavity remains a barrier to the treatment of oral cancer with photodynamic therapy (PDT). The greatest impediment to medical practitioners is the current need to shield the normal tissues of the oral cavity, a costly and time-consuming procedure. In this research, we present the design of illumination devices to deliver light to the oral cavity for PDT, which will facilitate administration of PDT in the clinic. The goal for such an illumination device, as indicated by our clinical collaborators at Roswell Park Cancer Institute in Buffalo, NY, is to limit exposure of healthy tissue and produce an average irradiance of 100 mW/cm2over the treatment field, with spatial non-uniformities below 10%. Furthermore, the size of the device must be compact to allow use in the oral cavity. Our research led to the design and fabrication of two devices producing spatial non-uniformities below 6% over a treatment area of 0.25 cm2by design. One device consisted of an appropriately-sized reflector, inspired by solar concentrators, illuminated by a cylindrical diffusing fiber optimally located within the reflector; another was a solid lightpipe with a combination of optimized tapered and straight components.