ArticleFrontiers in oncology2022
Novel image features of optical coherence tomography for pathological classification of lung cancer: Results from a prospective clinical trial.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 9 citations in OpenAlex.
- Segmental airway adenocarcinoma-simulating phantom for endoscopic near-infrared optical coherence tomography.Journal of biomedical optics · 2025Article
- OCT in Oncology and Precision Medicine: From Nanoparticles to Advanced Technologies and AI.Bioengineering (Basel, Switzerland) · 2025Review
- Advances in early detection of non-small cell lung cancer: A comprehensive review.Cancer medicine · 2024Review
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Authors and funding
11 authors at 2 institutions in 1 country.
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Abstract
Background: This study aimed to explore the characteristics of optical coherence tomography (OCT) imaging for differentiating between benign and malignant lesions and different pathological types of lung cancer in bronchial lesions and to preliminarily evaluate the clinical value of OCT. Methods: Patients who underwent bronchoscopy biopsy and OCT between February 2019 and December 2019 at the Chinese PLA General Hospital were enrolled in this study. White-light bronchoscopy (WLB), auto-fluorescence bronchoscopy (AFB), and OCT were performed at the lesion location. The main characteristics of OCT imaging for the differentiation between benign and malignant lesions and the prediction of the pathological classification of lung cancer in bronchial lesions were identified, and their clinical value was evaluated. Results: A total of 135 patients were included in this study. The accuracy of OCT imaging for differentiating between benign and malignant bronchial lesions was 94.1%, which was significantly higher than that of AFB (67.4%). For the OCT imaging of SCC, adenocarcinoma, and small-cell lung cancer, the accuracies were 95.6, 94.3, and 92%, respectively. The accuracy, sensitivity, and specificity of OCT were higher than those of WLB. In addition, these main OCT image characteristics are independent influencing factors for predicting the corresponding diseases through logistic regression analysis between the main OCT image characteristics in the study and the general clinical features of patients ( Conclusion: As a non-biopsy technique, OCT can be used to improve the diagnosis rate of lung cancer and promote the development of non-invasive histological biopsy.
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