ArticlePhotoacoustics2026
Vascular graph network for ovarian lesion classification using optical-resolution photoacoustic microscopy.
Article in Photoacoustics, 2026. 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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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
3 citing papers in PubMed.
- Effective point spread function modeling of photoacoustic remote sensing microscopy towards resolution enhancement.Photoacoustics · 2026Article
- Photoacoustic Imaging for Women's Gynecological Health: Advances and Clinical Prospects.Bioengineering (Basel, Switzerland) · 2026Review
- Characterization of vascular patterns in endometrial cancer via optical resolution photoacoustic microscopy.Journal of biomedical optics · 2026Article
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Authors and funding
9 authors.
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Abstract
Diagnosing ovarian lesions is challenging because of their heterogeneous clinical presentations. Some benign ovarian conditions, such as endometriosis, can have features that mimic cancer. We use optical-resolution photoacoustic microscopy (OR-PAM) to study the differences in ovarian vasculature between cancer and various benign conditions. In this study, we converted OR-PAM vascular data into vascular graphs augmented with physical vascular properties. From 94 ovarian specimens, a custom vascular graph network (VGN) was developed to classify each graph as either normal ovary, one of three benign pathologies, or cancer. We demonstrated for the first time that, by leveraging the intrinsic similarity between vascular networks and graph constructs, VGN provides stable predictions from sampling surface areas as small as 3 mm× 0.12 mm. In diagnosing cancer, VGN achieved 79.5 % accuracy and an area under the receiver operating characteristic curve (AUC) of 0.877. Overall, VGN achieved a five-class classification accuracy of 73.4 %.
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