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Developing algorithms for analyzing hyperspectral images as an intraoperative tool for margin assessment during breast-conserving surgery requires a dataset with reliable histopathologic labels. The feasibility of using tissue slices hyperspectral dataset with a high correlation with histopathology for developing an algorithm for analyzing the images from the surface of lumpectomy specimens was investigated. We presented a method to acquire hyperspectral images from the lumpectomy surface with a high correlation with histopathology. The tissue slices dataset was compared with the dataset obtained on lumpectomy specimen and the wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices were used to develop a tissue classification algorithm. Spectral differences were observed between tissue slices and lumpectomy datasets due to differences in the sample thickness between both datasets; wavelengths with a high penetration depth were able to penetrate through the thinner tissue slices, affecting the captured signal. By using only wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices, the adipose tissue could be discriminated from other tissue types, but differentiating malignant from connective tissue was more challenging.

applied sciences

Feasibility of Ex Vivo Margin Assessment with Hyperspectral Imaging during Breast-Conserving Surgery: From Imaging Tissue Slices to Imaging Lumpectomy Specimen