Abstract
ObjectiveTo explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).MethodsA total of 31 patients with pulmonary nodules were admitted to Department of Respiratory Medicine, Zhongda Hospital, Southeast University, and underwent chest CT, EB-OCT and biopsy. Attenuation coefficient and up to 56 different image features were extracted from A-line and B-scan of 1703 EB-OCT images. Attenuation coefficient and 29 image features with significant p-values were used to analyze the differences between normal and malignant samples. A RF classifier was trained using 70% images as training set, while 30% images were included in the testing set. The accuracy of the automated classification was validated by clinically proven pathological results.ResultsAttenuation coefficient and 29 image features were found to present different properties with significant p-values between normal and malignant EB-OCT images. The RF algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively.ConclusionIt is clinically practical to distinguish the nature of pulmonary nodules by integrating EB-OCT imaging with automated machine learning algorithm. Diagnosis of malignant pulmonary nodules by analyzing quantitative features from EB-OCT images could be a potentially powerful way for early detection of lung cancer.
Highlights
Pulmonary nodules are radiopaque densities seen in the lung parenchyma with a diameter of less than 3 cm1
The random forest (RF) algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively
It is clinically practical to distinguish the nature of pulmonary nodules by integrating endobronchial optical coherence tomography (OCT) (EB-OCT) imaging with automated machine learning algorithm
Summary
Pulmonary nodules are radiopaque densities seen in the lung parenchyma with a diameter of less than 3 cm. Various imaging techniques have been employed for in vivo early detection of lung cancer, such as endobronchial ultrasound (EBUS) [1], computed tomography (CT) [2], PET/CT and PET/MR [3]. All these techniques whose resolution are millimeter-level are far away from ideal resolution for detecting the nature of pulmonary nodules. The feasibility of EB-OCT to quantify separate airway wall layers and the satisfied correlation with histology and other imaging data have facilitated its application in the assessment of malignant pulmonary nodules [8]
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