ArticleDiagnostics (Basel, Switzerland)2020
Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-resistance by Feature Extraction of Gray Level Co-occurrence Matrix Using Optical Images.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 14 citations in OpenAlex.
- INSIDIA 2.0 High-Throughput Analysis of 3D Cancer Models: Multiparametric Quantification of Graphene Quantum Dots Photothermal Therapy for Glioblastoma and Pancreatic Cancer.International journal of molecular sciences · 2022Article
- Optical Diagnostics in Human Diseases.Diagnostics (Basel, Switzerland) · 2021Article
- Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM.Sensors (Basel, Switzerland) · 2021Article
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5 authors at 2 institutions in 1 country.
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Abstract
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 × 10
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