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Open AccessArticle

Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

1
Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan
2
School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
3
Department of Medicinal and Applied Chemistry, College of Life Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan
4
Department of Biomedical Engineering, National Cheng Kung University, Tainan 701, Taiwan
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(6), 389; https://doi.org/10.3390/diagnostics10060389
Received: 18 May 2020 / Revised: 5 June 2020 / Accepted: 7 June 2020 / Published: 9 June 2020
(This article belongs to the Special Issue Optical Diagnostics in Human Diseases)
Ovarian cancer is the most malignant of all gynecological cancers. A challenge that deteriorates with ovarian adenocarcinoma in neoplastic disease patients has been associated with the chemoresistance of cancer cells. Cisplatin (CP) belongs to the first-line chemotherapeutic agents and it would be beneficial to identify chemoresistance for ovarian adenocarcinoma cells, especially CP-resistance. Gray level co-occurrence matrix (GLCM) was characterized imaging from a numeric matrix and find its texture features. Serous type (OVCAR-4 and A2780), and clear cell type (IGROV1) ovarian carcinoma cell lines with CP-resistance were used to demonstrate GLCM texture feature extraction of images. Cells were cultured with cell density of 6 × 105 in a glass-bottom dish to form a uniform coverage of the glass slide to get the optical images by microscope and DVC camera. CP-resistant cells included OVCAR-4, A2780 and IGROV and had the higher contrast and entropy, lower energy, and homogeneity. Signal to noise ratio was used to evaluate the degree for chemoresistance of cell images based on GLCM texture feature extraction. The difference between wile type and CP-resistant cells was statistically significant in every case (p < 0.001). It is a promising model to achieve a rapid method with a more reliable diagnostic performance for identification of ovarian adenocarcinoma cells with CP-resistance by feature extraction of GLCM in vitro or ex vivo. View Full-Text
Keywords: chemoresistance; cisplatin; gray-level co-occurrence matrix; ovarian adenocarcinoma chemoresistance; cisplatin; gray-level co-occurrence matrix; ovarian adenocarcinoma
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Huang, C.-L.; Lian, M.-J.; Wu, Y.-H.; Chen, W.-M.; Chiu, W.-T. Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images. Diagnostics 2020, 10, 389.

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