Breast cancer is the second most common cancer worldwide and by far the most frequent cancer among women. A major limiting factor for complete surgical resection is the physician’s ability to intraoperatively assess presence of tumor positive resection margins. Many surgeons still rely on visual or tactile guidance and this leads in incomplete cancer resection rate that ranges between 20% and 50%. In this study we use multi-spectral Time-Resolved Fluorescence Spectroscopy (ms-TRFS), allowing for dynamic raster tissue scanning by merging a 450 nm aiming beam with the pulsed fluorescence excitation light in a single fiber collection. We developed a device that combines multispectral time resolved fluorescence lifetime with state-of-the-art machine learning techniques to delineate tumor margins of excised breast cancer specimen in real-time. In order to train the classifier, we precisely registered ex-vivo specimen with histology slides using fiducial markers and piecewise shape matching. A probabilistic random forest classifier was trained to rapidly delineate tumor regions. Moreover, the system not only provides binary output on tumor regions, but also quantifies the classifier’s certainty of each prediction. This allows the surgeon to either rescan the ambiguous area to increase certainty or extend the resection area to decrease the probability of positive tumor margins. The outcome is visualized by a simple color scheme showing tumor in red and adipose and fibrous tissue in blue and green and the certainty is encoded in color saturation. The system has been evaluated for n=10 lumpectomy specimen showing promising agreement between the classifier’s predictions and histology.
Jakob Unger, Christoph Hebisch, Jennifer Phipps, Richard Bold, Morgan Darrow, and Laura Marcu, "Real-time visualization of tumor margins in breast specimen using fluorescence lifetime (Conference Presentation)," Proc. SPIE 10472, Diagnosis and Treatment of Diseases in the Breast and Reproductive System IV, 104720H (Presented at SPIE BiOS: January 28, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2289619.5751469798001.
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