Structured Light Scatteroscopy for Intraoperative Breast Tumor Margin Assessment

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 DESCRIPTION (provided by applicant): The grant application responds to Program Announcement Number PA-14-147 (Ruth L. Kirschstein National Research Service Award for Individual Predoctoral Fellows) by proposing a study on utilizing optical scattering signatures for intraoperative breast tumor margin diagnosis using a newly invented, wide field, non-contact scatter imaging technique called structured light scatteroscopy imaging (SLSI). Breast conserving surgery (BCS) is a routine treatment for localized breast cancer, but there is a well-known issue of determining the boundaries of the tumor during its excision. The proposed system will rapidly produce wide field optical scatter images of the entire resected margin, which then will be run through previously trained classifier algorithms to yield maps of suspected breast tumor pathologies, to guide future resection procedures. An optimized, spectrally resolved SLSI system will be developed to meet imaging constraints of the operating room and the excision procedure. The finalized system will then be calibrated and validated on tissue simulating phantoms with known scattering parameters through a comparison of experimental measurements and Monte Carlo simulations of the phantoms. With the fully developed SLSI system, an ongoing collaboration with the National Institute of Standards and Technology and Dartmouth will amass measurements of freshly excised lumpectomy specimens, which will be co-registered to histopathological diagnoses. This data set will then be analyzed to determine diagnostically discriminate scatter parameters and image features. This library of scatter parameters and image features with accompanying breast pathology diagnoses will then be used to develop, train, and validate an optimal classification algorithm, which could then be used in real time during future BCS procedures to classify measurements of the tumor margin into maps of breast tumor margin with diagnostic probabilities. The efficacy of these algorithms will be evaluated by imaging freshly resected specimens of unknown diagnosis and performing receiver operator characteristic analysis on the classified probability maps. To realize the potential to reduce the rate of re-excision procedures, the complete system will then be implemented into a planned prospective clinical trial, which will be separately organized and managed through a separately funded Academic Industry Partnership. The Dartmouth team, involving engineering, surgery, pathology and feature classification expertise has completed multiple studies utilizing spectroscopic scatter imaging to classify breast tissue pathologies over the past 5 years, and the proposed system overcomes the shortcomings of the previously investigated systems and simultaneously leverages their benefits. The candidate will be trained in all pertinent aspects of this work to succeed in a surgical/pathology imaging systems research career, focused on novel tools to characterize breast cancer in situ.