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For solid tumors, such as breast cancer, surgery is usually the treatment of choice. For these operations, it is of utmost importance to remove the whole tumor, since tumor-positive resection margins may result in recurrent disease and impaired overall survival. Current optical imaging techniques using endogenous contrast - e.g. diffuse reflectance spectroscopy or optical coherence tomography – for detection of breast cancers are limited to point measurements or long acquisition times. Broadband hyperspectral cameras, which provide a complete spectral fingerprint of the object at pixel level, are needed for two-dimensional imaging of the operative region. An ex vivo study was conducted to evaluate the feasibility of hyperspectral imaging for breast cancer detection. Fresh (<3 hours after surgery) resected breast cancer slices were imaged with a snapshot hyperspectral camera, which has 41 spectral bands, equally distributed in the visible and near-infrared (VIS-NIR) range (450 – 950 nm). Supervised analysis was performed by using the pathology annotations and unsupervised analysis was performed by using the hierarchical stochastic neighbour embedding (h-SNE) algorithm. So far, nine resected specimens, of which six invasive carcinomas and two (partially) mucinous carcinomas, were imaged. Spectral differences were found between the non-tumor, malign and benign regions on the resected specimens. Furthermore, automatic feature classification using h-SNE was possible in selected cases. Hyperspectral imaging showed great potential for discrimination of benign and malign breast tissue by using specific wavelengths bands in an ex vivo setting. Further analysis will be performed to determine whether it is possible to select tumor-specific wavelengths.
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