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18 March 2019 Digital pathology with hyperspectral imaging for colon and ovarian cancer
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Morphological patterns of tissues are important index for pathologists to tell the difference between cancer and non-cancer cells. However, diagnoses with human eyes and experience have limitations. For example, ovarian cancers are categorized into 4 types in the morphological forms. This classification does not thoroughly correspond to the malignancy. Even worse, there are cases that medicines are not effective when patients have the same type of ovarian cancer. That is why, the new method to diagnose the cancer cells are demanded. In this paper, we measured and analyzed the hyperspectral data of colon cancer nuclei and ovarian cancer nuclei and proved that hyperspectral camera has potential to distinguish the cancer in the early stage and to find the novel classification which corresponds to the cancer malignancy. Machine learning methods enabled us to distinguish four stages of colon canceration with 98.9% accuracy. In addition, two groups of ovarian cancer specimens created based on the hyperspectral data showed a significant difference on their cumulative survival curves.
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Daiki Nakaya, Ayaka Tsutsumiuchi, Shin Satori, Makoto Saegusa, Tsutomu Yoshida, Ako Yokoi, and Masaki Kano "Digital pathology with hyperspectral imaging for colon and ovarian cancer", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560X (18 March 2019);

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