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- All Subjects: Microparticles
- All Subjects: Surgery
- Creators: Vernon, Brent
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
Polymer drug delivery system offers a key to a glaring issue in modern administration routes of drugs and biologics. Poly(lactic-co-glycolic acid) (PLGA) can be used to encapsulate drugs and biologics and deliver them into the patient, which allows high local concentration (compared to current treatment methods), protection of the cargo from the bodily environment, and reduction in systemic side effects. This experiment used a single emulsion technique to encapsulate L-tyrosine in PLGA microparticles and UV spectrophotometry to analyze the drug release over a period of one week. The release assay found that for the tested samples, the released amount is distinct initially, but is about the same after 4 days, and they generally follow the same normalized percent released pattern. The experiment could continue with testing more samples, test the same samples for a longer duration, and look into higher w/w concentrations such as 20% or 50%.
The goal of this research project is to create a Mathcad template file capable of statistically modelling the effects of mean and standard deviation on a microparticle batch characterized by the log normal distribution model. Such a file can be applied during manufacturing to explore tolerances and increase cost and time effectiveness. Theoretical data for the time to 60% drug release and the slope and intercept of the log-log plot were collected and subjected to statistical analysis in JMP. Since the scope of this project focuses on microparticle surface degradation drug release with no drug diffusion, the characteristic variables relating to the slope (n = diffusional release exponent) and the intercept (k = kinetic constant) do not directly apply to the distribution model within the scope of the research. However, these variables are useful for analysis when the Mathcad template is applied to other types of drug release models.
This thesis scrutinizes CLE technology for its ability to provide real-time intraoperative in vivo and ex vivo visualization of histopathological features of the normal and tumor brain tissues. First, the optimal settings for CLE imaging are studied in an animal model along with a generational comparison of CLE performance. Second, the ability of CLE to discriminate uninjured normal brain, injured normal brain and tumor tissues is demonstrated. Third, CLE was used to investigate cerebral microvasculature and blood flow in normal and pathological conditions. Fourth, the feasibility of CLE for providing optical biopsies of brain tumors was established during the fluorescence-guided neurosurgical procedures. This study established the optimal workflow and confirmed the high specificity of the CLE optical biopsies. Fifth, the feasibility of CLE was established for endoscopic endonasal approaches and interrogation of pituitary tumor tissue. Finally, improved and prolonged near wide-field fluorescent visualization of brain tumor margins was demonstrated with a scanning fiber endoscopy and 5-aminolevulinic acid.
These studies suggested a novel paradigm for neurosurgery-pathology workflow when the noninvasive intraoperative optical biopsies are used to interrogate the tissue and augment intraoperative decision making. Such optical biopsies could shorten the time for obtaining preliminary information on the histological composition of the tissue of interest and may lead to improved diagnostics and tumor resection. This work establishes a basis for future in vivo optical biopsy use in neurosurgery and planning of patient-related outcome studies. Future studies would lead to refinement and development of new confocal scanning technologies making noninvasive optical biopsy faster, convenient and more accurate.