acase@tulane.edu / New technology for ex vivo microscopy is needed to deliver point of care pathology, which can benefit margin assessment and biopsy screening for cancer management. Current permanent histopathology method requires fixation, sectioning and staining. This process is very labor intensive and time consuming which prevents pathology analysis at point of care. We believe that structured illumination microscopy (SIM) of rapidly stained ex vivo tissues can be used to address the needs in these areas. SIM is a wide-field optical sectioning technique that uses patterned illumination to preferentially modulate and retain the in-focus object information separately from the out-of-focus background. Its distinct advantage is that SIM is a wide-field technique, which is light efficient, and the speed is decoupled from the field of view. We will develop and optimize a video rate structured illumination microscope (VR-SIM) to potentially used for point of care pathology for cancer management.
Cancer resection surgery remains the primary intervention for most solid tumors. The complete surgical resection of the tumors associated with cure in nearly half of all patients. Incomplete surgical resection, as determined by the presence of residual tumor cells at the surface of the excised specimen, is the primary prognostic indicator for local tumor recurrence and decreased overall survival in a number of organ sites, where positive surgical margin (PSM) rates can exceed 50% for the most advanced tumors. Providing microscopic images of the entire tissue surface to the pathologist during the operation would enable detection of the presence and location of PSMs in time to correct the surgery and significantly reduce the need for harmful and costly salvage treatments. Unfortunately, the technical barrier to progress has been that no fluorescence microscopy method yet described for this application was fast enough to cover large tissue surface areas in 20 minutes, with the resolution to resolve nuclear atypia. When a patient presented with cancer related symptoms or a tumor, the next step in the work up for diagnosis is core needle biopsy, where typically 4-14 cores are obtained in a single biopsy session. However, the current false negative rate of the initial biopsy is up to 30% due to the lack of a fast and accurate point-of-care pathology tool. Diagnostic screening of biopsy tissue is also a concern in biospecimen banking for research purposes. The objective of the biobanking technician is to selectively obtain and preserve as much diseased tissue as possible. However, current limitations in rapid histopathology result in missed opportunities due to random sampling with no immediate feedback and wasted tissue due to destructive techniques for diagnostic confirmation.
The design criteria for a point of care pathology technology is:
• Be able to cover large tissue area with histological sensitivity within clinical relevant time frame
o Margins assessment: image large tissue surfaces ( 60 cm2) of the margin to identify PSMs
o Diagnostics Biopsy: image 8-14 biopsies of varying needle size
• Easy to use and non-destructive
The goal of this work is to optimize VR-SIM for accurate, high-throughput, non-destructive diagnostic imaging of fluorescently stained cancer biospecimens in point-of-care timeframes.
In this work, we developed a VR-SIM system to meet the needs of imaging thick tissue and validate the system with tissue-mimicking phantoms. We compared the performance of VR-SIM with confocal microscope in thick tissues, and investigated the use of different strategies to improve VR-SIM for large specimen imaging. We determined the optimal imaging parameters to achieve the optimum performance metrics (speed, resolution, and contrast) for point-of-care prostate biopsy and surgical margin imaging, and we conducted pilot clinical studies to evaluate the feasibility of VR-SIM imaging for in-procedure surgical and biopsy guidance. / 1 / Mei Wang
| Identifer | oai:union.ndltd.org:TULANE/oai:http://digitallibrary.tulane.edu/:tulane_77512 |
| Date | January 2017 |
| Contributors | Wang,Mei (author), Brown, J Quincy (Thesis advisor), School of Science & Engineering Biomedical Engineering (Degree granting institution) |
| Publisher | Tulane University |
| Source Sets | Tulane University |
| Language | English |
| Detected Language | English |
| Format | electronic, 207 |
| Rights | No embargo, Copyright is in accordance with U.S. Copyright law. |
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