ArticleCancers2020
Detection of Oral Dysplastic and Early Cancerous Lesions by Polarization-Sensitive Optical Coherence Tomography.
Article in Cancers, 2020. 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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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
11 citing papers in PubMed, 22 citations in OpenAlex.
- Potential screening of oral squamous cell carcinoma using microRNA geno-assay by electrochemical sensing: a new platform in biosensor technology.RSC advances · 2026Article
- Targeted Screening Strategies for Head and Neck Cancer: A Global Review of Evidence, Technologies, and Cost-Effectiveness.Diagnostics (Basel, Switzerland) · 2025Review
- Quantitative assessment of biomechanical changes in oral lesions at different cancerous stages using optical coherence elastography.Quantitative imaging in medicine and surgery · 2025Article
- Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence.Journal of clinical medicine · 2024Review
- Discrimination of healthy oral tissue from oral cancer based on the mean grey value determined by optical coherence tomography.BMC oral health · 2024Article
- Early Detection of Oral Potentially Malignant Disorders: A Review on Prospective Screening Methods with Regard to Global Challenges.Journal of maxillofacial and oral surgery · 2024Review
- En-face polarization-sensitive optical coherence tomography to characterize early-stage esophageal cancer and determine tumor margin.Biomedical optics express · 2022Article
- Human Umbilical Mesenchymal Stem Cell Xenografts Repair UV-Induced Photokeratitis in a Rat Model.Biomedicines · 2022Article
- Can we detect biomarkers of oral squamous cell carcinoma from saliva or mouth swabs?Evidence-based dentistry · 2022Article
- Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images.Radiology research and practice · 2022Review
- Research progress on the application of optical coherence tomography in the field of oncology.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Detection of oral dysplastic and early-stage cancerous lesions is difficult with the current tools. Half of oral cancers are diagnosed in a late stage. Detection of early stromal change to predict malignant transformation is a new direction in the diagnosis of early-stage oral cancer. The application of new optical tools to image stroma in vivo is under investigation, and polarization-sensitive optical coherence tomography (PS-OCT) is potentially one of those tools. This is a preliminary study to sequentially image oral stromal changes from normal, hyperplasia, and dysplasia to early-stage cancer by PS-OCT in vivo. We used 4-Nitroquinoline-1-oxide drinking water to induce dysplasia and early-stage oral cancer in 19 K14-EGFP-miR-211-GFP transgenic mice. A total of 8 normal, 12 hyperplastic, 11 dysplastic, and 4 early-stage cancerous lesions were enrolled. A new analytic process of PS-OCT imaging was proposed, called an en-face birefringence map. From the birefringence map, the sensitivity, specificity, positive predictive value, and negative predictive values to detect dysplasia and early-stage cancer were 100.00%, 95.00%, 93.75%, and 100.00%, respectively, and the kappa value of these images between two investigators was 0.942. The mean size of malignant lesions detected in this study is 1.66 ± 0.93 mm. This pilot animal study validates the use of PS-OCT to detect small and early-stage oral malignancy with high accuracy and consistency.
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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.