ArticleNature biomedical engineering2026
Panoramic photoacoustic computed tomography with learning-based classification enhances breast lesion characterization.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Robust hybrid feature-driven on-the-fly mapping enables freehand 3D panoramic photoacoustic angiography.Light, science & applications · 2026Article
- A generative adversarial network with multi-scale structural features for sparse-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Controlled sweat generation via ultrasound stimulation integrated in a wearable device.Nature communications · 2026Article
- Bandgap-enabled ultrasound tissue marking by a biodegradable metastructured hydrogel implant.Science advances · 2026Article
- Super-stable two-dimensional radical conjugated metal-organic frameworks for efficient NIR-II photothermal conversion.Science advances · 2026Article
- Utilizing post-imaging surveys to guide development of photoacoustic computed technology for breast imaging.American journal of surgery · 2026Article
- Dynamic microvascular monitoring with miniaturized omnidirectional broadband photoacoustic imaging system for living entities (MOBILE).Nature communications · 2026Article
- Panoramic photoacoustic computed tomography with learning-based classification enhances breast lesion characterization.Nature biomedical engineering · 2026Article
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- High-speed photoacoustic and ultrasonic computed tomography of the breast tumor for early diagnosis with enhanced accuracy.Science advances · 2025Article
- Advances in Photoacoustic Imaging of Breast Cancer.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Breast cancer diagnosis is crucial due to the high prevalence and mortality rate associated with the disease. However, mammography involves ionizing radiation and has compromised sensitivity in radiographically dense breasts, ultrasonography lacks specificity and has operator-dependent image quality, and magnetic resonance imaging faces high cost and patient exclusion. Photoacoustic computed tomography (PACT) offers a promising solution by combining light and ultrasound for high-resolution imaging that detects tumour-related vasculature changes. Here we introduce a workflow using panoramic PACT for breast lesion characterization, offering detailed visualization of vasculature irrespective of breast density. Analysing PACT features of 78 breasts in 39 patients, we develop learning-based classifiers to distinguish between normal and suspicious tissue, achieving a maximum area under the receiver operating characteristic curve of 0.89, which is comparable with that of conventional imaging standards. We further differentiate malignant and benign lesions using 13 features. Finally, we developed a learning-based model to segment breast lesions. Our study identifies PACT as a non-invasive and sensitive imaging tool for breast lesion evaluation.
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Registered trials
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