ArticleAcademic radiology2020
Fully Automated Postlumpectomy Breast Margin Assessment Utilizing Convolutional Neural Network Based Optical Coherence Tomography Image Classification Method.
Article in Academic radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Artificial intelligence in the radiologic assessment of ductal carcinomaFrontiers in oncology · 2026Pooled it
- Deep-learning-aided sarcoma biopsy guidance using forward-view endoscopic optical coherence tomography.Biomedical optics express · 2026Article
- Effective transfer of tumor annotations from hematoxylin and eosin to fluorescence images of breast and lung tissues.Journal of biomedical optics · 2026Article
- Deep UV laser scanning microscopy with integrated 1050 nm spectral-domain optical coherence tomography for multi-contrast tissue imaging.Biomedical optics express · 2025Article
- Clinical evaluation of breast cancer tissue with optical coherence tomography: key findings from a large-scale study.Journal of cancer research and clinical oncology · 2025Article
- Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images.Sensors (Basel, Switzerland) · 2024Article
- The LMIT: Light-mediated minimally-invasive theranostics in oncology.Theranostics · 2024Review
- Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography.Cancers · 2023Article
- Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo.Journal of cancer research and clinical oncology · 2023Article
- 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 holds promise to transform the diagnostic anatomic pathology gross evaluation process.Journal of biomedical optics · 2022Article
- Binary dose level classification of tumour microvascular response to radiotherapy using artificial intelligence analysis of optical coherence tomography images.Scientific reports · 2022Article
- Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning.Biomedical optics express · 2022Article
- Development of intraoperative assessment of margins in breast conserving surgery: a narrative review.Gland surgery · 2022Review
- Research progress on the application of optical coherence tomography in the field of oncology.Frontiers in oncology · 2022Review
- Millimeter-scale chip-based supercontinuum generation for optical coherence tomography.Science advances · 2021Article
- 3-D compressed sensing optical coherence tomography using predictive coding.Biomedical optics express · 2021Article
- Optical palpation for tumor margin assessment in breast-conserving surgery.Biomedical optics express · 2021Article
- Towards an Optical Biopsy during Visceral Surgical Interventions.Visceral medicine · 2020Review
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe purpose of this study was to develop a deep learning classification approach to distinguish cancerous from noncancerous regions within optical coherence tomography (OCT) images of breast tissue for potential use in an intraoperative setting for margin assessment.
methodsA custom ultrahigh-resolution OCT (UHR-OCT) system with an axial resolution of 2.7 μm and a lateral resolution of 5.5 μm was used in this study. The algorithm used an A-scan-based classification scheme and the convolutional neural network (CNN) was implemented using an 11-layer architecture consisting of serial 3 × 3 convolution kernels. Four tissue types were classified, including adipose, stroma, ductal carcinoma in situ, and invasive ductal carcinoma.
resultsThe binary classification of cancer versus noncancer with the proposed CNN achieved 94% accuracy, 96% sensitivity, and 92% specificity. The mean five-fold validation F1 score was highest for invasive ductal carcinoma (mean standard deviation, 0.89 ± 0.09) and adipose (0.79 ± 0.17), followed by stroma (0.74 ± 0.18), and ductal carcinoma in situ (0.65 ± 0.15).
conclusionIt is feasible to use CNN based algorithm to accurately distinguish cancerous regions in OCT images. This fully automated method can overcome limitations of manual interpretation including interobserver variability and speed of interpretation and may enable real-time intraoperative 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.