Aerosols as diagnostic tools

Deciphering Exhaled Aerosol Fingerprints for Early Diagnosis and Personalized Therapeutics of Obstructive Respiratory Diseases in Small Airways

Respiratory diseases often show no apparent symptoms at their early stages and are usually diagnosed when permanent damages have been made to the lungs. A major site of lung pathogenesis is the small airways, which make it highly challenging to detect using current techniques due to the diseases’ location (inaccessibility to biopsy) and size (below normal CT/MRI resolution). In this review, we present a new method for lung disease detection and treatment in small airways based on exhaled aerosols, whose patterns are uniquely related to the health of the lungs. Proof-of-concept studies are first presented in idealized lung geometries. We subsequently describe the recent developments in feature extraction and classification of the exhaled aerosol images to establish the relationship between the images and the underlying airway remodeling. Different feature extraction algorithms (aerosol density, fractal dimension, principal mode analysis, and dynamic mode decomposition) and machine learning approaches (support vector machine, random forest, and convolutional neural network) are elaborated upon. Finally, future studies and frequent questions related to clinical applications of the proposed aerosol breath testing are discussed from the authors’ perspective. The proposed breath testing has clinical advantages over conventional approaches, such as easy-to-perform, non-invasive, providing real-time feedback, and is promising in detecting symptomless lung diseases at early stages.

The Effect of Aging on Aerosol Bolus Deposition in the Healthy Adult Lung: A 19-Year Longitudinal Study

Background: While it is recognized that peripheral lung structure and ventilation heterogeneity change with age, the effects of age on aerosol deposition in the healthy adult lung is largely unknown. Methods: A series of aerosol bolus inhalations were repeatedly performed in four healthy subjects over a period of 19 years (years = 0, 9, 15 and 19). For each series, a bolus of 1 μm particles was inhaled at penetration volumes (V_p) ranging from 200 to 1200 mL. Aerosol bolus deposition (DE), dispersion (H), and mode shift (MS) were calculated along with the rate of increase in these parameters with increasing V_p (slope-DE, slope-H, and slope-MS). Results: Slope-DE significantly increased from 0.040 ± 0.014 (mean ± standard deviation) at year 0 to 0.069 ± 0.007%/mL at year 19 (p = 0.02) with no significant difference in DE at shallow depth (V_p = 200 mL; 14% ± 4% at year 0 vs. 15% ± 7% at year 19, p = 0.25). There was no significant effect of age on either slope-H (0.44 ± 0.05 at year 0 vs. 0.47 ± 0.09 mL/mL at year 19, p = 0.6) or dispersion at shallow depth (192 ± 36 mL at year 0 vs. 220 ± 54 mL at year 19, p = 0.2). Slope-MS became significantly more negative with increasing age (-0.096 ± 0.044 at year 0 vs. -0.171 ± 0.027 mL/mL at year 19, p = 0.001) with no significant difference in MS at shallow depth (12 ± 10 at year 0 vs. 7 ± 15 mL at year 19, p = 0.3). Conclusions: These data suggest that (1) peripheral deposition increases with aging in the healthy lung, likely as a result of increasing closing volume with age; (2) alterations in the mechanical properties of healthy adult lungs with age occur uniformly; and (3) the significant increase in the magnitude of MS-slope with age is likely due to the concomitant increase in peripheral deposition and possible alterations in flow sequencing.

Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

Background

Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases.

Objective and Methods

In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions.

Findings

By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations.

Conclusion

For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases.

CFD modeling and image analysis of exhaled aerosols due to a growing bronchial tumor: towards non-invasive diagnosis and treatment of respiratory obstructive diseases

Diagnosis and prognosis of tumorigenesis are generally performed with CT, PET, or biopsy. Such methods are accurate, but have the limitations of high cost and posing additional health risks to patients. In this study, we introduce an alternative computer aided diagnostic tool that can locate malignant sites caused by tumorigenesis in a non-invasive and low-cost way. Our hypothesis is that exhaled aerosol distribution is unique to lung structure and is sensitive to airway structure variations. With appropriate approaches, it is possible to locate the disease site, determine the disease severity, and subsequently formulate a targeted drug delivery plan to treat the disease. This study numerically evaluated the feasibility of the proposed breath test in an image-based lung model with varying pathological stages of a bronchial squamous tumor. Large eddy simulations and a Lagrangian tracking approach were used to model respiratory airflows and aerosol dynamics. Respirations of tracer aerosols of 1 µm at a flow rate of 20 L/min were simulated, with the distributions of exhaled aerosols recorded on a filter at the mouth exit. Aerosol patterns were quantified with multiple analytical techniques such as concentration disparity, spatial scanning and fractal analysis. We demonstrated that a growing bronchial tumor induced notable variations in both the airflow and exhaled aerosol distribution. These variations became more apparent with increasing tumor severity. The exhaled aerosols exhibited distinctive pattern parameters such as spatial probability, fractal dimension, and multifractal spectrum. Results of this study show that morphometric measures of the exhaled aerosol pattern can be used to detect and monitor the pathological states of respiratory diseases in the upper airway. The proposed breath test also has the potential to locate the site of the disease, which is critical in developing a personalized, site-specific drug delivery protocol.

Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis

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 µm 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.

Potential Role of Lung Ventilation Scintigraphy in the Assessment of COPD

OBJECTIVE

To highlight the importance of the lung ventilation scintigraphy (LVS) to study the regional distribution of lung ventilation and to describe most frequent abnormal patterns of lung ventilation distribution obtained by this technique in COPD and to compare the information obtained by LVS with the that obtained by traditional lung function tests.

MATERIAL AND METHODS

The research was done in 20 patients with previously diagnosed COPD who were treated in Intensive care unit of Clinic for pulmonary diseases and TB "Podhrastovi" Clinical Center, University of Sarajevo in exacerbation of COPD during first three months of 2014. Each patient was undergone to testing of pulmonary function by body plethysmography and ventilation/perfusion lung scintigraphy with radio pharmaceutics Technegas, 111 MBq Tc -99m-MAA. We compared the results obtained by these two methods.

RESULTS

All patients with COPD have a damaged lung function tests examined by body plethysmography implying airflow obstruction, but LVS indicates not only airflow obstruction and reduced ventilation, but also indicates the disorders in distribution in lung ventilation.

CONCLUSION

LVS may add further information to the functional evaluation of COPD to that provided by traditional lung function tests and may contribute to characterizing the different phenotypes of COPD.

Optimum peripheral drug deposition in patients with cystic fibrosis

In order to identify the optimum particle size and breathing pattern for high peripheral deposition of inhaled drugs in patients with cystic fibrosis, regional deposition in these patients was studied systematically as a function of particle size, inhalation volume and flow rate. Regional deposition was assessed using the single-breath regional deposition technique in which the concentration profile of inhaled and exhaled non-radioactive, monodisperse test particles is analyzed. Using this technique particle deposition within the functional dead space volume and peripherally can be assessed. Regional deposition was measured in 12 patients with cystic fibrosis using 2, 3, 4, and 5.5 microm particles, inhalation volumes of 500, 1000, 1500, and 2000 cm(3), and inhalation flow rates of 100, 250, 500, and 750 cm(3)/sec. Peripheral deposition was highest when 2-3-microm particles were inhaled with air-flow rates of 250-500 cm(3)/sec. With these parameters peripheral deposition increased with increasing inhalation volume and reached values of about 60% of the total drug inhaled. It has been shown that high peripheral drug deposition can be achieved in patients with CF when inhalations are performed using an optimized combination of particle size and breathing pattern.

Saline aerosol bolus dispersion. II. The effect of conductive airway alteration

In a companion study (Verbanck S, Schuermans D, Vincken W, and Paiva M, J Appl Physiol 90: 1754-1762, 2001), we investigated whether saline aerosol bolus tests could also be used to detect proximal, as opposed to peripheral, airway alterations. We studied 10 never-smokers before and after histamine challenge, obtaining, for various volumetric lung depths (VLD), saline bolus-derived indexes computed by discarding aerosol concentrations below either 50% of the exhaled bolus maximum (half-width, H) or below cutoffs ranging from 5 to 25% (standard deviation, sigma(5%)-sigma(25%)) and skew (sk(5)-sk(25%)). Multiple-breath N(2) washout-derived indexes of conductive (S(cond)) and acinar (S(acin)) ventilation inhomogeneity were also determined. After histamine, S(cond) significantly increased (P = 0.008) whereas S(acin) remained unaffected, indicating purely conductive airway alteration. Consistent with this observation, sk(5%) (or sk(25%)) was increased to the same extent at all VLD, and sigma(5%) was increased preferentially at low VLD. By contrast, H and sigma(25%) displayed preferential increases at high VLD, a pattern similar to that induced by peripheral alterations. The present work shows that proximal airway alteration can be reliably identified by saline bolus tests only if these include measurements at low and high VLD and if bolus dispersion is quantified as a standard deviation with a low cutoff.

Aerosol bolus dispersion and aerosol-derived airway morphometry: assessment of lung pathology and response to therapy, Part 1

review discusses the potential utility of two methods using inhaled aerosols to detect and diagnose lung disease and to evaluate the efficacy of therapy. Aerosol bolus dispersion measures convective gas mixing; aerosol-derived airway morphometry assesses the calibers of airway and airspaces. These two methods are discussed in terms of their ease of use (simplicity and acceptability) and current data regarding their validity, reproducibility, specificity, sensitivity, and detection of lung improvement with therapy. Part 1 of this review focuses upon aerosol bolus dispersion; Part 2(1) focuses upon aerosol-derived airway morphometry. Aerosol bolus dispersion has many features that make it clinically attractive. It is simple to administer and patients can successfully perform the maneuvers. It detects known alterations in the lungs. It is reproducible and has high specificity and sensitivity. However, every lung disease or condition known to be detected by aerosol bolus dispersion is also detected by spirometery, maximal expiratory flow-volume curves, or another conventional lung function test. This, aerosol bolus dispersion appears best reserved as a specialized method to supplement conventional lung function tests and to characterize convective gas transport.