Creation of an Autonomous, Fluorescence-Based Tissue Diagnostic and Ablation Device for use in Brain Tumor Resection

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2020

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Abstract

Physical resection of tumors is a crucial component of the treatment of brain tumors. Traditionally, a multi-faceted approach involving resection as well as chemotherapy and radiation provide the backbone for most modern brain tumor treatment strategies. The process of tumor location and removal routinely involves the use of a number of sensory modalities (such as pre-operative MRIs and CT scans) and manual resection tools (such as electrocautery and forceps). Unfortunately, this current paradigm offers hurdles that stand in the way of more precise, accurate tumor resection. These hurdles include the physical phenomenon of brain shift and the natural limitation of the human hand placement in surgery. Due to the well documented relationship between extent of resection of brain tumor and survivability, there is a need to overcome these hurdles for more precise surgical resection. This dissertation presents a device that aims to increase the accuracy and precision of tumor removal surgery. This integrated system involves a non-contact laser induced fluorescence device, called the TumorID, and the previously documented TumorCNC. The TumorID utilizes a 405 nm laser to induce fluorescence that is collected by the device and quantified by an attached CCD spectrometer. The quantified spectral data is passed to a trained classifier that classifies the data as healthy or tumorous. The designation is passed to the TumorCNC. Based on the designation and sensory data, the TumorCNC generates an ablation path and removes tissue using a CO2 laser. The TumorID is capable of classifying melanoma brain metastasis, glioma, and healthy tissue with 100% accuracy based on ex vivo mouse brain tissue. The total integrated system is capable of ablating the boundary of a tumor mimicking tissue phantom with a RMSE of 1.69 mm. Therefore, on average for the entire tumor boundary, the device only deviates from the actual tumor boundary by approximately 1.5 mm. Reports have indicated that human surgeons can achieve accuracy on the order of approximately 0.3 mm. Therefore, the system is still short of the reported accuracy of a human surgeon. However, future research steps include the incorporation of a more sophisticated search strategy, implementation of a classifier that utilizes the boundary as a class in the mutli-class classifier, and decreasing the spot size of the CO2 laser. All of these potential avenues have the potential to increase the accuracy and precision of the tumor removal abilities of the non-contact, integrated system.

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Tucker, Matthew (2020). Creation of an Autonomous, Fluorescence-Based Tissue Diagnostic and Ablation Device for use in Brain Tumor Resection. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/22159.

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