ArticlePhotoacoustics2025
Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images.
Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence based on ultrasound for initial diagnosis of malignant ovarian cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Photoacoustic-ultrasound endoscopy for assessment of rectal cancer treatment response: A prospective study with T2-weighted MRI radiomics comparison.Photoacoustics · 2026Article
- Characterization of vascular patterns in endometrial cancer via optical resolution photoacoustic microscopy.Journal of biomedical optics · 2026Article
- Integrated spectral and depth compensation approach for optimizing oxygen saturation and total hemoglobin estimation in photoacoustic tomography for ovarian lesion diagnosis.Journal of biomedical optics · 2026Article
- Vascular graph network for ovarian lesion classification using optical-resolution photoacoustic microscopy.Photoacoustics · 2026Article
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Abstract
Ovarian-adnexal lesions are conventionally assessed with ultrasound (US) under the guidance of the Ovarian-Adnexal Reporting and Data System (O-RADS). However, the low specificity of O-RADS results in many unnecessary surgeries. Here, we use co-registered US and photoacoustic tomography (PAT) to improve the diagnostic accuracy of O-RADS. Physics-based parametric algorithms for US and PAT were developed to estimate the acoustic and photoacoustic properties of 93 ovarian lesions. Additionally, statistics-based radiomic algorithms were applied to quantify differences in the lesion texture on US-PAT images. A machine learning model (US-PAT KNN model) was developed based on an optimized subset of eight US and PAT imaging features to classify a lesion as either cancer, one of four subtypes of benign lesions, or a normal ovary. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.969 and a balanced six-class classification accuracy of 86.0 %.
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