Ms. Shelby Joan Skidmore Profile

Shelby Joan Skidmore

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Proceedings Article | 13 February 2018 Presentation + Paper

Multi-modality photoacoustic tomography, ultrasound, and light sheet microscopy for volumetric tumor margin detection

Gurneet Sangha, Bihe Hu, Daniel Bolus, Mei Wang, Shelby Skidmore, Andrew Sholl, J. Quincy Brown, Craig Goergen

Proceedings Volume 10487, 104870D (2018) https://doi.org/10.1117/12.2289047

KEYWORDS: Tissues, Acquisition tracking and pointing, Tumors, Biopsy, Breast, Breast cancer, Ultrasonography, Microscopy, Photoacoustic tomography, Cancer

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Current methods for breast tumor margin detection are invasive, time consuming, and typically result in a reoperative rate of over 25%. This marks a clear clinical need to develop improved tools to intraoperatively differentiate negative versus positive tumor margins. Here, we utilize photoacoustic tomography (PAT), ultrasound (US), and inverted Selective Plane Illumination Microscopy (iSPIM) to assess breast tumor margins in eight human breast biopsies. Our PAT/US system consists of a tunable Nd:YAG laser (NT 300, EKSPLA) coupled with a 40MHz central frequency US probe (Vevo2100, FUJIFILM Visual Sonics). This system allows for the delivery of 10Hz, 5ns pulses with fluence of 40mJ/cm² to the tissue with PAT and US axial resolutions of 125μm and 40μm, respectively. For this study, we used a linear stepper motor to acquire volumetric PAT/US images of the breast biopsies using 1100nm light to identify bloodrich “tumor” regions and 1210nm light to identify lipid-rich “healthy” regions. iSPIM (Applied Scientific Instrumentation) is an advanced microscopy technique with lateral resolution of 1.5μm and axial resolution of 7μm. We used 488nm laser excitation and acridine orange as a general comprehensive histology stain. Our results show that PAT/US can be used to identify lipid-rich regions, dense areas of arterioles and arteries, and other internal structures such as ducts. iSPIM images correlate well with histopathology slides and can verify nuclear features, cell type and density, stromal features, and microcalcifications. Together, this multimodality approach has the potential to improve tumor margin detection with a high degree of sensitivity and specificity.

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