ArticleBiomedical optics express2021
Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 40 citations in OpenAlex.
- Polarisation-Sensitive Optical Coherence Tomography for Surgical Margin Evaluation and Diagnosis of Excised Canine Cutaneous and Subcutaneous Tumours.Veterinary and comparative oncology · 2026Article
- Margin Matters: Advances in Intraoperative Margin Assessment for Breast-Conserving Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Deep UV laser scanning microscopy with integrated 1050 nm spectral-domain optical coherence tomography for multi-contrast tissue imaging.Biomedical optics express · 2025Article
- Three-dimensional dynamic optical coherence tomography for breast tumor margin assessment.Biomedical optics express · 2025Article
- Polarisation Sensitive Optical Coherence Tomography Image Characteristics for Gastrointestinal Tumours and Normal Tissues at Surgical Margins in Dogs.Veterinary and comparative oncology · 2025Article
- Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography.Biomedical optics express · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine.Journal of personalized medicine · 2023Review
- Single-input polarization-sensitive optical coherence tomography through a catheter.Biomedical optics express · 2023Article
- Compression OCT-elastography combined with speckle-contrast analysis as an approach to the morphological assessment of breast cancer tissue.Biomedical optics express · 2023Article
- Article
- Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Optical Coherence Tomography Angiography of the Intestine: How to Prevent Motion Artifacts in Open and Laparoscopic Surgery?Life (Basel, Switzerland) · 2023Article
- En-face polarization-sensitive optical coherence tomography to characterize early-stage esophageal cancer and determine tumor margin.Biomedical optics express · 2022Article
- Comparative study of OCTA algorithms with a high-sensitivity multi-contrast Jones matrix OCT system for human skin imaging.Biomedical optics express · 2022Article
- Local axis orientation mapped by polarization sensitive optical coherence tomography provides a unique contrast to identify caries lesions in enamel.Biomedical optics express · 2022Article
- Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning.Biomedical optics express · 2022Article
- Simultaneous directional full-field OCT using path-length and carrier multiplexing.Optics express · 2021Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We report an automated differentiation model for classifying malignant tumor, fibro-adipose, and stroma in human breast tissues based on polarization-sensitive optical coherence tomography (PS-OCT). A total of 720 PS-OCT images from 72 sites of 41 patients with H&E histology-confirmed diagnoses as the gold standard were employed in this study. The differentiation model is trained by the features extracted from both one standard OCT-based metric (i.e., intensity) and four PS-OCT-based metrics (i.e., phase difference between two channels (
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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.