ArticleCancer informatics2017
Epithelial Ovarian Cancer Diagnosis of Second-Harmonic Generation Images: A Semiautomatic Collagen Fibers Quantification Protocol.
Article in Cancer informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 22 citations in OpenAlex.
- Impact of tumor necrosis factor-alpha and lysophosphatidic acid on the behavior of ovarian cancer cells in a three-dimensional collagen hydrogel.The journal of obstetrics and gynaecology research · 2025Article
- Segmentation-free Radon transform algorithm to detect orientation and size of tissue structures in multiphoton microscopy images.Journal of biomedical optics · 2025Article
- Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.The Analyst · 2023Article
- Accurate evaluation of the treatment effects of immunotherapy on subcutaneous ovarian cancer in mice with nonlinear optical imaging and algorithmic analysis.Biomedical optics express · 2022Article
- A FIJI macro for quantifying pattern in extracellular matrix.Life science alliance · 2021Article
- Spatial frequency metrics for analysis of microscopic images of musculoskeletal tissues.Connective tissue research · 2021Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
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Abstract
A vast number of human pathologic conditions are directly or indirectly related to tissular collagen structure remodeling. The nonlinear optical microscopy second-harmonic generation has become a powerful tool for imaging biological tissues with anisotropic hyperpolarized structures, such as collagen. During the past years, several quantification methods to analyze and evaluate these images have been developed. However, automated or semiautomated solutions are necessary to ensure objectivity and reproducibility of such analysis. This work describes automation and improvement methods for calculating the anisotropy (using fast Fourier transform analysis and the gray-level co-occurrence matrix). These were applied to analyze biopsy samples of human ovarian epithelial cancer at different stages of malignancy (mucinous, serous, mixed, and endometrial subtypes). The semiautomation procedure enabled us to design a diagnostic protocol that recognizes between healthy and pathologic tissues, as well as between different tumor types.
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