Because contemporary intraoperative tumor detection modalities, such as intraoperative MRI, are not ubiquitously available and can disrupt surgical workflow, there is an imperative for an accessible diagnostic device that can meet the surgeon’s needs in identifying tissue types. The objective of this paper is to determine the efficacy of a novel non-contact tumor detection device for metastatic melanoma boundary identification in a tissue-mimicking phantom, evaluate the identification of metastatic melanoma boundaries in ex vivo mouse brain tissue, and find the error associated with identifying this boundary. To validate the spatial and fluorescence resolution of the device, tissue-mimicking phantoms were created with modifiable optical properties. Phantom tissue provided ground truth measurements for fluorophore concentration differences with respect to spatial dimensions. Modeling metastatic disease, ex vivo melanoma brain metastases were evaluated to detect differences in fluorescence between healthy and neoplastic tissue. This analysis includes determining required-to-observe fluorescence differences in tissue. H&E staining confirmed tumor presence in mouse tissue samples. The device detected a difference in normalized average fluorescence intensity in all three phantoms. There were differences in fluorescence with the presence and absence of melanin. The estimated tumor boundary of all tissue phantoms was within 0.30 mm of the ground truth tumor boundary for all boundaries. Likewise, when applied to the melanoma-bearing brains from ex vivo mice, a difference in normalized fluorescence intensity was successfully detected. The potential prediction window for the tumor boundary location is less than 1.5 mm for all ex vivo mouse brain tumors boundaries. We present a non-contact, laser-induced fluorescence device that can identify tumor boundaries based on changes in laser-induced fluorescence emission intensity. The device can identify phantom ground truth tumor boundaries within 0.30 mm using instantaneous rate of change of normalized fluorescence emission intensity and can detect endogenous fluorescence differences in melanoma brain metastases in ex vivo mouse tissue.
The ability to differentiate healthy and tumorous tissue is vital during the surgical removal of tumors. This ability is especially critical during neurosurgical tumor resection due to the risk associated with removing healthy brain tissue. In this paper, we present an epifluorescence spectroscopy guided device that is not only capable of autonomously classifying a region of tissue as tumorous or healthy in real-time–but is also able to differentiate between different tumor types. For this study, glioblastoma and melanoma were chosen as the two different tumor types. Six mice were utilized in each of the three classes (healthy, glioblastoma, melanoma) for a total of eighteen mice. A “one-vs-the-all” approach was used to create a multi-class classifier. The multi-class classifier was capable of classifying with 100% accuracy. Future work includes increasing the number of mice in each of the three tumor classes to create a more robust classifier and expanding the number of tumor types beyond glioblastoma and melanoma.
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