Exhaled Aerosol Pattern Discloses Lung Structural Abnormality: A Sensitivity Study Using Computational Modeling and Fractal Analysis

Lin E-B (2014) Exhaled Aerosol Pattern Discloses Lung Structural Abnormality: A Sensitivity Study Using Computational Modeling and Fractal Analysis. PLoS ONE 9(8): e104682. doi:10.1371/journal.pone.0104682 Exhaled Aerosol Pattern Discloses Lung Structural Abnormality: A Sensitivity Study Using Computational Modeling and Fractal Analysis Jinxiang Xi 0 Xiuhua A. Si 0 JongWon Kim 0 Edward Mckee 0 En-Bing Lin 0 Francisco J. Esteban, University of Jaen, Spain 0 1 School of Engineering and Technology, Central Michigan University , Mount Pleasant, Michigan , United States of America, 2 Science Division, Calvin College , Grand Rapids, Michigan , United States of America, 3 College of Medicine, Central Michigan University , Mount Pleasant, Michigan , United States of America, 4 Department of Mathematics, Central Michigan University , Mount Pleasant, Michigan , United States of America Background: Exhaled aerosol patterns, also called aerosol fingerprints, provide clues to the health of the lung and can be used to detect disease-modified airway structures. The key is how to decode the exhaled aerosol fingerprints and retrieve the lung structural information for a non-invasive identification of respiratory diseases. Objective and Methods: In this study, a CFD-fractal analysis method was developed to quantify exhaled aerosol fingerprints and applied it to one benign and three malign conditions: a tracheal carina tumor, a bronchial tumor, and asthma. Respirations of tracer aerosols of 1 mm at a flow rate of 30 L/min were simulated, with exhaled distributions recorded at the mouth. Large eddy simulations and a Lagrangian tracking approach were used to simulate respiratory airflows and aerosol dynamics. Aerosol morphometric measures such as concentration disparity, spatial distributions, and fractal analysis were applied to distinguish various exhaled aerosol patterns. Findings: Utilizing physiology-based modeling, we demonstrated substantial differences in exhaled aerosol distributions among normal and pathological airways, which were suggestive of the disease location and extent. With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest. Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum. Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma. Conclusion: Aerosol-fingerprint-based breath tests disclose clues about the site and severity of lung diseases and appear to be sensitive enough to be a practical tool for diagnosis and prognosis of respiratory diseases with structural abnormalities. - Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files. Funding: This work was funded by Central Michigan University Innovative Research Grant P421071 and Early Career Grant P622911. XS was supported by Calvin Summer Research Grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Accurate and early diagnosis of lung cancer is crucial to patients survivability. For instance, patients with non-small cell lung cancer have a cure rate of more than 70% when diagnosed at Stage I whereas less than 25% if diagnosed at Stage III [1]. Conventional methods of diagnosing lung diseases or cancers include pulmonary function tests using the chest X-ray for screening, CT/PET/SPET for examining abnormal structures, and sputum cytology or lung tissue biopsy for evaluating the type and extent of the cancer [2]. These diagnosis procedures are generally reliable, but are costly and require professional operations. Moreover, some procedures are invasive and pose radiation risks to patients. Recently, an alternative diagnosis method using a patients exhaled breath has been developed based on the premise that exhalation contains clues to many diseases [3]. Metabolic changes of growing cancer cells cause changes in the production of certain chemicals and generate a unique breath fingerprint, which can be used to determine whether a disease is present. Studies have reported elevated levels of nitric oxide in relation with asthma [4], antioxidants with chronic obstructive pulmonary disease (COPD) [5], chemokines with cystic fibrosis [6], and isoprene with non-small cell lung cancer (NSCLC) [7]. Reviews on evidence supporting lung cancer diagnosis using breath tests and related developments of breath devices can be found in [8,9]. These breath devices are often small in size, noninvasive, easy to use, less expensive, and hold the promise of efficient diagnosis of lung cancer and other respiratory diseases. In spite of these advantages, gas-signature based breath devices only measure the presence and concentration of exhaled gas chemicals. They do not provide information on where these chemicals are produced (the cancer site) or the level of airway remodeling, both of which are crucial in cancer treatment planning. The site and degree of airway remodeling can be substantially different for different lung cancers (Fig. 1a). Any alternative that can locate the malignant sites in a safer and less expensive way would be highly desirable. Currently, this information can only be obtained with the help of radiological techniques such as CT or PET. A number of studies have explored the use of aerosols as a lung diagnostic tool, such as the aerosol bolus dispersion (ABD) method [10,11,12]. However, the ABD method does not provide new information about the lung function compared to existing pulmonary function tests [12]. More recently, Xi et al. [13] proposed a new aerosol breath test that has the potential to detect the disease and locate its site. This method arises from persistent observations of unique deposition patterns with respect to prescribed geometry and breathing conditions [14,15,16]. We hypothesize that each airway structure has a signature aerosol fingerprint (AFP), as opposed to the gas fingerprint discussed before. Accordingly, any deviation from the normal pattern may indicate an abnormality inside the airway, which can be retrieved with an inverse numerical approach developed by Xi et al. [17]. The subsequent questions are: how can we quantitate the exhaled AFP patterns from different airway geometries? Will the exhaled AFPs be sensitive enough to detect airway structural changes? More importantly, how can we use this information to predict the presence and location of airway abnormalities based on samples of exhaled aerosol profiles? In this study, fractal analysis will be implemented to quantitate the complex patterns of exhaled aerosol fin (...truncated)