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Proceedings Paper

Digital pathology with hyperspectral imaging for colon and ovarian cancer
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Paper Abstract

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.

Paper Details

Date Published: 18 March 2019
PDF: 7 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560X (18 March 2019); doi: 10.1117/12.2512328
Show Author Affiliations
Daiki Nakaya, Happy Science Univ. (Japan)
Ayaka Tsutsumiuchi, Happy Science Univ. (Japan)
Shin Satori, Happy Science Univ. (Japan)
Makoto Saegusa, Kitasato Univ. (Japan)
Tsutomu Yoshida, Kitasato Univ. (Japan)
Ako Yokoi, Kitasato Univ. (Japan)
Masaki Kano, Digi-Tapir Inc. (Japan)


Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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