ArticleBiomedical optics express2021
Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning.Scientific reports · 2024Article
- Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model.Biomedical optics express · 2024Article
- On the quantitative analysis of lamellar collagen arrangement with second-harmonic generation imaging.Biomedical optics express · 2024Article
- Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.Polish journal of radiology · 2024Review
- Use and application of organ-on-a-chip platforms in cancer research.Journal of cell communication and signaling · 2023Review
- Article
- Current Understanding on Why Ovarian Cancer Is Resistant to Immune Checkpoint Inhibitors.International journal of molecular sciences · 2023Review
- A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women.Diagnostics (Basel, Switzerland) · 2023Article
- Nonlinear microscopy and deep learning classification for mammary gland microenvironment studies.Biomedical optics express · 2023Article
- CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.Journal of ovarian research · 2023Article
- Review
- Organ on Chip Technology to Model Cancer Growth and Metastasis.Bioengineering (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Regarding growth pattern and cytological characteristics, borderline ovarian tumors fall between benign and malignant, but they tend to develop malignancy. Currently, it is difficult to accurately diagnose ovarian cancer using common medical imaging methods, and histopathological examination is routinely used to obtain a definitive diagnosis. However, such examination requires experienced pathologists, being labor-intensive, time-consuming, and possibly leading to interobserver bias. By using second-harmonic generation imaging and k-nearest neighbors classifier in conjunction with automated machine learning tree-based pipeline optimization tool, we developed a computer-aided diagnosis method to classify ovarian tissues as being malignant, benign, borderline, and normal, obtaining areas under the receiver operating characteristic curve of 1.00, 0.99, 0.98, and 0.97, respectively. These results suggest that diagnosis based on second-harmonic generation images and machine learning can support the rapid and accurate detection of ovarian cancer in clinical practice.
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Registered trials
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