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

Journal Article · · Theranostics
DOI:https://doi.org/10.7150/thno.11107· OSTI ID:1195576
 [1];  [1];  [2];  [3];  [3];  [4]
  1. Central Michigan Univ., Mount Pleasant, MI (United States)
  2. California Baptist Univ., Riverside, CA (United States)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  4. First Affiliated Hospital of Xi'an Medical Univ., Shaanxi (China); Mayo Clinic, Rochester, MN (United States)
+ Show Author Affiliations

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.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1195576
Journal Information:
Theranostics, Vol. 5, Issue 5; ISSN 1838-7640
Publisher:
IvyspringCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 16 works
Citation information provided by
Web of Science

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Cited By (5)

Magnetic aerosol drug targeting in lung cancer therapy using permanent magnet journal January 2019
Structural and functional alterations of the tracheobronchial tree after left upper pulmonary lobectomy for lung cancer journal October 2019
Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning journal January 2019
Computational Approaches in Theranostics: Mining and Predicting Cancer Data journal March 2019
Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification journal September 2015

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62 RADIOLOGY AND NUCLEAR MEDICINE
aerosol breath test
computer aided diagnosis
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aerosol fingerprint
fractal dimension
obstructive respiratory diseases