Positive margin status after breast-conserving surgery (BCS) is a predictor of higher rates of local recurrence. Intraoperative margin assessment aims to achieve negative surgical margin status at the first operation, thus reducing the re-excision rates that are usually associated with potential surgical complications, increased medical costs, and mental pressure on patients. Microscopy with ultraviolet surface excitation (MUSE) can rapidly image tissue surfaces with subcellular resolution and sharp contrasts by utilizing the nature of the thin optical sectioning thickness of deep ultraviolet light. We have previously imaged 66 fresh human breast specimens that were topically stained with propidium iodide and eosin Y using a customized MUSE system. To achieve objective and automated assessment of MUSE images, a machine learning model is developed for binary (tumor vs. normal) classification of obtained MUSE images. Features extracted by texture analysis and pre-trained convolutional neural networks (CNN) have been investigated for sample descriptions. A sensitivity, specificity, and accuracy better than 90% have been achieved for detecting tumorous specimens. The result suggests the potential of MUSE with machine learning being utilized for intraoperative margin assessment during BCS.
Keywords: Breast-conserving surgery; fluorescence microscopy; machine learning; tumor margin; ultraviolet.