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

Diagnostics

Chih-Ling Huang¹  Wen-Tai Chiu²  Yi-Hsuan Wu³  Meng-Jia Lian⁴  Wei-Ming Chen⁴ 

[1] Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan;Department of Biomedical Engineering, National Cheng Kung University, Tainan 701, Taiwan;Department of Medicinal and Applied Chemistry, College of Life Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan;School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan;

关键词: chemoresistance;    cisplatin;    gray-level co-occurrence matrix;    ovarian adenocarcinoma;   

DOI  :  10.3390/diagnostics10060389

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown