ArticleTranslational oncology2020
Ultrasound Guided Optoacoustic Tomography in Assessment of Tumor Margins for Lumpectomies.
Article in Translational oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Assessment of Breast Tumors Using Multispectral Optoacoustic Tomography in a Surgical Setting.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Trial
- Investigation of feasibility and diagnostic capability of ultrasound-guided photoacoustic tomography for breast surgical margins.Npj imaging · 2026Article
- Development of a Dual Photoacoustic-Ultrasound Imaging System: Current Status and Future Perspectives.Sensors (Basel, Switzerland) · 2026Review
- Ultrasound-guided photoacoustic (US-PA) tomography of the breast: Biochemical differentiation using intrinsic tissue markers-lipids, collagen and hemoglobin with histopathologic correlation.Scientific reports · 2024Article
- Structural and functional imaging of psoriasis for severity assessment and quantitative monitoring of treatment response using high-resolution optoacoustic imaging.Photoacoustics · 2024Article
- Biochemical "decoding" of breast ultrasound images with optoacoustic tomography fusion: First-in-human display of lipid and collagen signals on breast ultrasound.Photoacoustics · 2022Article
- Assessment of photoacoustic tomography contrast for breast tissue imaging using 3D correlative virtual histology.Scientific reports · 2022Article
- Clinical photoacoustic/ultrasound dual-modal imaging: Current status and future trends.Frontiers in physiology · 2022Review
Corrections and comments
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Authors and funding
24 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo determine the accuracy of a handheld ultrasound-guided optoacoustic tomography (US-OT) probe developed for human deep-tissue imaging in ex vivo assessment of tumor margins postlumpectomy.
methodsA custom-built two-dimensional (2D) US-OT-handheld probe was used to scan 15 lumpectomy breast specimens. Optoacoustic signals acquired at multiple wavelengths between 700 and 1100 nm were reconstructed using model linear algorithm, followed by spectral unmixing for lipid and deoxyhemoglobin (Hb). Distribution maps of lipid and Hb on the anterior, posterior, superior, inferior, medial, and lateral margins of the specimens were inspected for margin involvement, and results were correlated with histopathologic findings. The agreement in tumor margin assessment between US-OT and histopathology was determined using the Bland-Altman plot. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of margin assessment using US-OT were calculated.
resultsNinety margins (6 × 15 specimens) were assessed. The US-OT probe resolved blood vessels and lipid up to a depth of 6 mm. Negative and positive margins were discriminated by marked differences in the distribution patterns of lipid and Hb. US-OT assessments were concordant with histopathologic findings in 87 of 89 margins assessed (one margin was uninterpretable and excluded), with diagnostic accuracy of 97.9% (kappa = 0.79). The sensitivity, specificity, PPV, and NPV were 100% (4/4), 97.6% (83/85), 66.7% (4/6), and 100% (83/83), respectively.
conclusionUS-OT was capable of providing distribution maps of lipid and Hb in lumpectomy specimens that predicted tumor margins with high sensitivity and specificity, making it a potential tool for intraoperative tumor margin assessment.
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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.