ArticleBiomedical optics express2019
Classification of human ovarian cancer using functional, spectral, and imaging features obtained from in vivo photoacoustic imaging.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Artificial intelligence based on ultrasound for initial diagnosis of malignant ovarian cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Seedling fibroid characterization using optical coherence tomography.Biomedical optics express · 2026Article
- Characterization of vascular patterns in endometrial cancer via optical resolution photoacoustic microscopy.Journal of biomedical optics · 2026Article
- Feasibility of the photoacoustic detection of haemophilia-related microbleeds: a preclinical study using isolated pig limbs.Thrombosis journal · 2026Article
- Vascular graph network for ovarian lesion classification using optical-resolution photoacoustic microscopy.Photoacoustics · 2026Article
- Optical and Optoacoustic Imaging.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- A primer on current status and future opportunities of clinical optoacoustic imaging.Npj imaging · 2025Review
- Mini review of photoacoustic clinical imaging: a noninvasive tool for disease diagnosis and treatment evaluation.Journal of biomedical optics · 2024Review
- Dual-modal Photoacoustic and Ultrasound Imaging: from preclinical to clinical applications.Frontiers in photonics · 2024Article
- Photoacoustic clinical applications: Musculoskeletal and abdominal imaging.Zeitschrift fur medizinische Physik · 2023Review
- Fabrication of a translational photoacoustic needle sensing probe for interstitial photoacoustic spectral analysis.Photoacoustics · 2023Article
- Cylindrical lens configuration for optimizing light delivery in a curvilinear endocavity photoacoustic imaging system.Optics letters · 2023Article
- Hybrid magnetic resonance and optoacoustic tomography (MROT) for preclinical neuroimaging.Light, science & applications · 2022Article
- A review of co-registered transvaginal photoacoustic and ultrasound imaging for ovarian cancer diagnosis.Current opinion in biomedical engineering · 2022Article
- Characterizing the aggressiveness of prostate cancer using an all-optical needle photoacoustic sensing probe: feasibility study.Biomedical optics express · 2021Article
- Photoacoustic tomography reconstruction using lag-based delay multiply and sum with a coherence factor improves in vivo ovarian cancer diagnosis.Biomedical optics express · 2021Article
- Role of blood oxygenation saturation in ovarian cancer diagnosis using multi-spectral photoacoustic tomography.Journal of biophotonics · 2021Article
- Photoacoustic imaging biomarkers for monitoring biophysical changes during nanobubble-mediated radiation treatment.Photoacoustics · 2020Article
- Photoacoustic laser effects in live mouse blastocysts: pilot safety studies of DNA damage from photoacoustic imaging doses.F&S science · 2020Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We report in this pilot study the diagnostic results of in vivo imaging of patients with ovarian lesions, using a co-registered photoacoustic and ultrasound (PAT/US) system. A total of 39 ovaries from 24 patients were imaged in vivo. PAT functional features, i.e., blood oxygen saturation (sO2) and relative total hemoglobin (rHbT), PAT image features, and PAT spectral features within a region of interest (ROI) in each ovarian tissue were extracted. To select the significant features, a t-test on each feature was performed, and the independent predictors were determined by evaluating correlation between each pair of predictors. To classify the ovarian lesions, we employed a generalized linear model (GLM) and a support vector machine (SVM). We used these classifiers first to distinguish benign/normal lesions from ovaries with invasive epithelial tumors and then to separate normal/benign lesions from all types of ovarian tumors. We developed classifiers once by inclusion of PAT functional features to assess the best diagnostic performance of the classifiers when multiple wavelengths data are available. Second time, we excluded the PAT functional features from the features set to evaluate the best diagnostic performance if only a single wavelength is available. Our results show that using functional features improves the classification performance, especially for distinguishing normal/benign ovarian lesions from all types of tumors. In this case, an area under ROC curve (AUC) of 0.92, 0.93 of testing data was achieved using a GLM and SVM classifier when functional features were included in the feature set while excluding these features resulted in an AUC of 0.89, 0.92, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.