Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm

Ming Ding, Shi-yu Pan, Jing Huang, Cheng Yuan, Qiang Zhang, Xiao-li Zhu and Yan Cai

PLOS ONE, 2021, vol. 16, issue 12, 1-15

Abstract: Objective: To 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). Methods: A 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. Results: Attenuation 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. Conclusion: It 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0260600

DOI: 10.1371/journal.pone.0260600

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