{
    "abstract": "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).\nA 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.\nAttenuation 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.\nIt 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.",
    "authors": [
        {
            "affiliation": "Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China.",
            "firstname": "Ming",
            "initials": "M",
            "lastname": "Ding"
        },
        {
            "affiliation": "School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.",
            "firstname": "Shi-Yu",
            "initials": "SY",
            "lastname": "Pan"
        },
        {
            "affiliation": "Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China.",
            "firstname": "Jing",
            "initials": "J",
            "lastname": "Huang"
        },
        {
            "affiliation": "Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China.",
            "firstname": "Cheng",
            "initials": "C",
            "lastname": "Yuan"
        },
        {
            "affiliation": "Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China.",
            "firstname": "Qiang",
            "initials": "Q",
            "lastname": "Zhang"
        },
        {
            "affiliation": "Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China.",
            "firstname": "Xiao-Li",
            "initials": "XL",
            "lastname": "Zhu"
        },
        {
            "affiliation": "School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.",
            "firstname": "Yan",
            "initials": "Y",
            "lastname": "Cai"
        }
    ],
    "conclusions": "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.",
    "copyrights": null,
    "doi": "10.1371/journal.pone.0260600\n10.1378/chest.07-1357\n10.1024/0040-5930/a000305\n10.1007/s00464-017-5543-8\n10.1164/rccm.200712-1776OC\n10.1378/chest.10-0753\n10.5858/arpa.2012-0252-SA\n10.3791/3855\n10.1016/j.rmed.2015.09.006\n10.1364/BOE.6.004191\n10.1097/LBR.0000000000000491\n10.1159/000503261\n10.1364/AO.58.00A135\n10.1002/jbio.201800255\n10.1364/BOE.7.005182\n10.1016/s0735-1097(01)01799-5\n10.1109/TMI.2015.2461533\n10.1371/journal.pone.0110690\n10.1007/s11517-018-1878-0\n10.1016/j.lungcan.2005.04.007\n10.1364/AO.32.006032\n10.1002/jbio.202000364\n10.1007/s10103-014-1541-4\n10.1117/1.3280271\n10.1364/BOE.4.001014\n10.1177/32.11.6386975\n10.1109/42.141636\n10.1378/chest.11-2948\n10.1136/bmj.317.7170.1411\n10.6004/jnccn.2015.0071\n10.1111/resp.12980\n10.1111/1759-7714.13446\n10.1016/j.chest.2019.10.042\n10.1097/LBR.0b013e31823fcf11\n10.1093/jjco/hyz025\n10.1016/j.chest.2016.07.033\n10.1378/chest.13-0534",
    "journal": "PloS one",
    "keywords": [],
    "methods": null,
    "publication_date": "2022-01-01",
    "pubmed_id": "34971557\n17873165\n22753287\n28409370\n18310475\n20472863\n23194041\n23381470\n26427628\n26504665\n29659420\n31593955\n30873970\n30318761\n28018734\n11849858\n26241970\n25329322\n30076537\n15922488\n20856430\n33314731\n24532117\n20210431\n23847728\n6386975\n18218367\n23546484\n9822393\n25964637\n28177181\n32342673\n31738928\n23207256\n30855687\n27522957\n23828441",
    "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.",
    "title": "Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.",
    "xml": "<Element 'PubmedArticle' at 0x7885054f3ce0>"
}