Audio Technology To Detect Lung Cancer Earlier

Study Overview
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Brief Summary
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A cross-sectional study of prospectively collected cough audio recordings using spectral analysis.

Detailed Description
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In the UK lung cancer is the leading cause of cancer death, also, UK survival rates are poorer compared to other European countries. Lung cancer is plagued by late presentation; 70% present with advanced incurable disease and a third die within 90 days of diagnosis. As such, there is a clear and urgent need to achieve earlier diagnosis of lung cancer. Symptomatic presentation is the most common route to lung cancer diagnosis and symptoms may be present for many months before diagnosis, even in early stage disease. The most common (68%) presenting symptom is cough. Unfortunately cough is also common with other illnesses. Furthermore, a high proportion of those at high-risk of lung cancer have pre-existing cough or respiratory disease (e.g. ex- or current- smokers, patients with chronic obstructive pulmonary disease (COPD)). Cough sounds are known to vary according to underlying lung pathology and could therefore have diagnostic value. Potentially, there may be unique cough and/or respiratory sounds or patterns associated with lung cancer that are not detectable by the human audio spectrum. Identification of a tool that accurately discriminates lung cancer cough could be pivotal. This is a prospective cross-sectional study that will involve subjective analysis of spectrograms of cough recorded from individuals with normal lungs, individuals at high-risk for lung cancer (COPD and other chronic lung diseases) and individuals with lung cancer. 24 hour ambulatory audio recordings will be prospectively collected from patients attending respiratory medicine clinics at Queen Elizabeth University Hospital (QEUH), Glasgow. Participants will be given a free-field lapel microphone and mp3 recorder for 24h. The Leicester Cough Monitor (LCM) will be used to extract the cough sounds from the 24h recordings. The LCM is an automated cough detection system that was developed by Dr Surinder Birring (Kings College Hospital). It uses an algorithm to automatically identify cough sounds from audio recordings, which is then able to provide data on cough frequency. As part of this process, the LCM splices out 1 second sound clips for all parts of the audio recordings that are identified as being (i) a cough sound or (ii) a non-cough sound.

Official Title
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Audio Technology To Detect Lung Cancer Earlier

Conditions
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Lung Neoplasms, Cough

Intervention / Treatment
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* Diagnostic Test: Leicester Cough Monitor (LCM)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Aged 50 years and above and who are either: Normal Smokers - individuals who have presented with cough but who appear to have 'healthy' lungs (i.e. COPD, other chronic lung disease and lung cancer have been excluded after clinical assessment); COPD - individuals with a confirmed diagnosis of COPD according to established criteria; Other (non-COPD) chronic lung disease - individuals with a confirmed diagnosis of non-COPD chronic lung disease (e.g. pulmonary fibrosis, asthma); Lung cancer - individuals with confirmed diagnosis of lung cancer including disease in the lungs and an active cough Exclusion Criteria: Participants with an active or recent lung infection, as defined by either the production of purulent sputum associated with systemic symptoms or fever, and/or the receipt of antibiotics for lung infection or acute exacerbation over the 2 weeks preceding the date of consent Participants who are unable to provide informed consent Participants who are receiving/have previously received radiotherapy to the lungs Participants who are currently receiving chemotherapy

Ages Eligible for Study
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Minimum Age: 50 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Lung cancer<br>Individuals with confirmed diagnosis of lung cancer including disease in the lungs and an active cough. | Diagnostic Test: Leicester Cough Monitor (LCM)<br>* The Leicester Monitoring Cough (LCM) kit includes a digital flash voice recorder (Sony ICD-PX333), aA clip-on lapel microphone (Philips LFH9173) and a small carry/travel waist bag.<br>|
| COPD<br>Individuals with a confirmed diagnosis of COPD according to established criteria. | Diagnostic Test: Leicester Cough Monitor (LCM)<br>* The Leicester Monitoring Cough (LCM) kit includes a digital flash voice recorder (Sony ICD-PX333), aA clip-on lapel microphone (Philips LFH9173) and a small carry/travel waist bag.<br>|
| Other (non-COPD) chronic lung disease<br>Individuals with a confirmed diagnosis of non-COPD chronic lung disease (e.g. pulmonary fibrosis, asthma). | Diagnostic Test: Leicester Cough Monitor (LCM)<br>* The Leicester Monitoring Cough (LCM) kit includes a digital flash voice recorder (Sony ICD-PX333), aA clip-on lapel microphone (Philips LFH9173) and a small carry/travel waist bag.<br>|
| Normal smokers<br>Individuals who have presented with cough but who appear to have 'healthy' lungs (i.e. COPD, other chronic lung disease and lung cancer have been excluded after clinical assessment). | Diagnostic Test: Leicester Cough Monitor (LCM)<br>* The Leicester Monitoring Cough (LCM) kit includes a digital flash voice recorder (Sony ICD-PX333), aA clip-on lapel microphone (Philips LFH9173) and a small carry/travel waist bag.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Spectral centroid (Hz) measurement | Spectral centroid (Hz) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Spectral bandwith (Hz) measurement | Spectral bandwith (Hz) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral crest factor (Hz) measurement | Spectral crest factor (Hz) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral flatness (adimensional) measurement | Spectral flatness (adimensional) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral flux (Watts²) measurement | Spectral flux (Watts²) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral roll-off (Hz) measurement | Spectral roll-off (Hz) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Ratio f50 vs f90 (adimensional) measurement | Ratio f50 vs f90 (adimensional) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral peak entropy (adimensional measurement) | Spectral peak entropy (adimensional measurement) of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral Renyi entropy (adimensional) measurement | Spectral Renyi entropy (adimensional) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral kurtosis (adimensional) measurement | Spectral kurtosis (adimensional) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral skewness (adimensional) measurement | Spectral skewness (adimensional) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months | 
| Spectral entropy (adimensional) measurement | Spectral entropy (adimensional) measurement of cough sounds to ascertain any similarities or differences in spectral characteristics between samples | 12 months |