## 1 Introduction  
Breast-conserving surgery in combination with adjuvant radiotherapy is the preferred local treatment for women with breast cancer [1, 2, 3]. During these surgeries, surgeons aim to remove the complete tumor while sparing as much healthy tissue as possible. This, however, becomes challenging when no clear tumor border can be defined during surgery, and in up to 29% of the surgeries, residual tumor is left behind in the patient [4, 5, 6, 7, 8, 9]. Currently, a pathologist evaluates if the tumor is completely removed by analyzing the resection margin of the lumpectomy specimen under a microscope. As this involves extensive processing of the tissue, this requires a few days and no direct feedback can be given to the surgeon during surgery. Therefore, these patients require a second operation or extra radiotherapy boost to clear tumor deposits left behind during the initial procedure. A technique that offers great potential for margin assessment during surgery is hyperspectral imaging: The intrinsic optical properties of the entire resection surface can be imaged fast, over a wide field of view, without tissue contact. Hyperspectral imaging measures diffuse reflected light after it has undergone multiple scattering and absorption events in the tissue [10]. Therefore, a hyperspectral image is generated that contains both spectral and spatial information of the tissue, which can be used to discriminate different tissue types. The spectral information represents the optical properties of the tissue, i.e., the composition and morphology, and the spatial information provides a reference of the spectral information in the imaged scene. Hyperspectral imaging has shown to be useful in the detection of cancer in ex vivo human tissue in head and neck [11; 12], colon [13] and breast [14; 15], and in vivo during brain surgery [16]. Developing and testing a reliable tissue classification algorithm for hyperspectral images requires a labeled dataset. To acquire such a dataset with trustworthy labels, the hyperspectral images need to be correlated with histopathology results. When investigating the potential of hyperspectral imaging for intraoperative breast cancer detection, specifically, there is the tradeoff between obtaining a high correlation with histopathology and the aim to image the actual resection surface to allow for intraoperative feedback. When the actual resection surface is measured with hyperspectral imaging, a correlation with histopathological information is difficult as the tissue highly deforms during histopathological processing and the histopathological sections are obtained perpendicular to the resection surface, covering only a small fraction of the entire surface [17]. In addition, the chance of measuring tumor positive margins is low as currently, at the time of measurements, there is no feedback on whether a margin is tumor positive or negative. Therefore, previous research of our group investigated the potential of hyperspectral imaging for cancer detection by measuring breast tissue slices that were obtained after inking and gross-sectioning of the resection specimen. Therefore, we were able to ensure a high correlation of the optical measurements with histopathological information and obtain enough measurements on both healthy and tumorous tissue to develop reliable classification algorithms. With this extensive database, we developed and tested classification algorithms and showed that hyperspectral imaging has the potential to discriminate tumor from healthy tissue with a sensitivity and specificity higher than 98% [15]. In this study, we image the resection surface of lumpectomy specimens and present a method to obtain a high correlation of these measurements with histopathology. Second, we compare the hyperspectral dataset obtained on tissue slices with the dataset obtained on lumpectomy specimens to analyze if we can use the classification algorithm developed on the tissue slices to classify the lumpectomy data.