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Trial registered on ANZCTR
Registration number
ACTRN12613001141730
Ethics application status
Approved
Date submitted
3/10/2013
Date registered
14/10/2013
Date last updated
14/10/2013
Type of registration
Prospectively registered
Titles & IDs
Public title
Improving Prediction of Outcomes from Lung Cancer Surgery Using Quantitative Computed Tomography
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Scientific title
In patients undergoing resection of lung cancer, how well does quantitative computed tomography, compared to tests of pulmonary function and exercise capacity, predict postoperative outcomes?
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Secondary ID [1]
283199
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Nil known
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Universal Trial Number (UTN)
U1111-1147-9063
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Trial acronym
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Non-small cell lung cancer
290061
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Chronic obstructive pulmonary disease
290086
0
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Emphysema
290087
0
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Condition category
Condition code
Cancer
290436
290436
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0
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Lung - Non small cell
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Respiratory
290463
290463
0
0
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Chronic obstructive pulmonary disease
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Respiratory
290688
290688
0
0
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Other respiratory disorders / diseases
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Intervention/exposure
Study type
Observational
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Patient registry
False
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Target follow-up duration
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Target follow-up type
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Description of intervention(s) / exposure
Quantitative CT software provides the ability to measure airway wall thickness on chest CT images, which is a potentially useful measure of airway obstruction. We intend to analyse participants' CT images using quantitative CT to determine airway wall thickness and attenuation values in the individual lobes. We hope to add these variables to the conventional measures used to predict postoperative outcomes in order to determine if this will be a useful tool to contribute to the prediction of postoperative outcomes. The CT images which will be used include a preoperative CT scan as well as a low-dose CT scan 6 months postoperatively for each patient.
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Intervention code [1]
287925
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Not applicable
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Comparator / control treatment
All participants in this study will be receiving the usual standard of clinical care with respect to the treatment of their lung cancer. We will only be selecting patients who will be undergoing resection of their lung cancer. The use of quantitative CT measures will therefore be compared to the usual tests used to determine eligibility for surgery (pulmonary function tests and six minute walk distance). These tests will be performed preoperatively and 6 months postoperatively.
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Control group
Active
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Outcomes
Primary outcome [1]
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Quality of life score as determined by the EORTC QLQ-C30 and EORTC QLQ-LC13 questionnaires
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Assessment method [1]
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Timepoint [1]
290473
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Baseline and at 6 months following lung resection
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Primary outcome [2]
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All cause mortality as determined from medical records and other sources
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Assessment method [2]
290474
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Timepoint [2]
290474
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6 months following lung resection
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Secondary outcome [1]
304569
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Accuracy of prediction of postoperative lung function based on quantitative CT measures of attenuation and airway wall thickness
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Assessment method [1]
304569
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Timepoint [1]
304569
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6 months following lung resection
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Eligibility
Key inclusion criteria
-Histologically confirmed non-small cell lung cancer
-Pulmonary resection to treat the lung cancer, in the form of pneumonectomy, lobectomy or limited resection
-Available CT images compatible with quantitative CT software
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Minimum age
18
Years
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Maximum age
No limit
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Sex
Both males and females
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Can healthy volunteers participate?
No
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Key exclusion criteria
-Inability to provide informed consent
-Inability to attend follow up at 6 months
-Inability to speak English
-Pregnant women
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Study design
Purpose
Natural history
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Duration
Longitudinal
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Selection
Defined population
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Timing
Prospective
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Statistical methods / analysis
Statistical power was calculated by assuming that linear regression will be used to determine the association between Pi10, which is a measure of airway wall thickness, and quality of life, represented by the EORTC QLQ-C30 score. The estimated standard deviations for these variables were determined based on previous studies by other investigators. We determined that a sample size of 50 would be sufficient to provide 80% power at a significance level of 0.05.
We intend to employ multiple regression to determine the effect of multiple variables (such as pulmonary function tests, six minute walk distance, airway wall thickness) on quality of life scores and mortality. Linear regression will be used to determine the accuracy of prediction of postoperative lung function based on quantitative CT measures.
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Recruitment
Recruitment status
Not yet recruiting
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Date of first participant enrolment
Anticipated
15/10/2013
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Actual
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Date of last participant enrolment
Anticipated
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Actual
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Date of last data collection
Anticipated
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Actual
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Sample size
Target
50
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Accrual to date
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Final
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Recruitment in Australia
Recruitment state(s)
QLD
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Recruitment hospital [1]
1510
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The Prince Charles Hospital - Chermside
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Recruitment postcode(s) [1]
7350
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4032 - Chermside
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Funding & Sponsors
Funding source category [1]
287964
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Hospital
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Name [1]
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The Prince Charles Hospital
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Address [1]
287964
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The Prince Charles Hospital
Rode Rd
Chermside
QLD 4032
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Country [1]
287964
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Australia
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Funding source category [2]
287965
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University
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Name [2]
287965
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The University of Queensland
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Address [2]
287965
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The University of Queensland
St Lucia
QLD 4072
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Country [2]
287965
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Australia
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Primary sponsor type
Hospital
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Name
The Prince Charles Hospital
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Address
The Prince Charles Hospital
Rode Rd
Chermside
QLD 4032
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Country
Australia
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Secondary sponsor category [1]
286683
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University
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Name [1]
286683
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The University of Queensland
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Address [1]
286683
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The University of Queensland
St Lucia
QLD 4072
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Country [1]
286683
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Australia
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Ethics approval
Ethics application status
Approved
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Ethics committee name [1]
289888
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The Prince Charles Hospital HREC
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Ethics committee address [1]
289888
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The Prince Charles Hospital Administration Building, Lower Ground Rode Rd Chermside QLD 4032
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Ethics committee country [1]
289888
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Australia
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Date submitted for ethics approval [1]
289888
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25/07/2013
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Approval date [1]
289888
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09/08/2013
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Ethics approval number [1]
289888
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HREC/13/QPCH/218
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Summary
Brief summary
This study is evaluating whether quantitative computed tomography (CT) can enable us to more accurately predict postoperative outcomes in patients undergoing lung cancer surgery. Who is it for? You may be eligible to join this study if you are aged 18 years or above and have been diagnosed with non-small cell lung cancer for which you will undergo lung resection surgery. Study details All participants in this study will receive standard care by their treating physicians and quantitative CT software will be used to analyse their CT images. Quantitative CT provides the ability to measure airway wall thickness on chest CT images, which is a potentially useful measure of airway obstruction. We hope to add these variables to the conventional measures used to predict postoperative outcomes in order to determine if this will be a useful tool to contribute to the prediction of postoperative outcomes, including quality of life and mortality. Prediction of postoperative outcomes following lung resection for lung cancer is important because it enables the selection of suitable surgical candidates.
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Trial website
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Trial related presentations / publications
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Public notes
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Contacts
Principal investigator
Name
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Mr Xiang-Wen Lee
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Address
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Department of Thoracic Medicine
The Prince Charles Hospital
Rode Rd
Chermside
QLD 4032
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Country
42866
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Australia
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Phone
42866
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+61450102822
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Fax
42866
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Email
42866
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[email protected]
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Contact person for public queries
Name
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Xiang-Wen Lee
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Address
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Department of Thoracic Medicine
The Prince Charles Hospital
Rode Rd
Chermside
QLD 4032
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Country
42867
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Australia
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Phone
42867
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+61450102822
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Fax
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Email
42867
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[email protected]
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Contact person for scientific queries
Name
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Kwun Fong
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Address
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Department of Thoracic Medicine
The Prince Charles Hospital
Rode Rd
Chermside
QLD 4032
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Country
42868
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Australia
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Phone
42868
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+61731394314
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Fax
42868
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Email
42868
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[email protected]
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No information has been provided regarding IPD availability
What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
Documents added manually
No documents have been uploaded by study researchers.
Documents added automatically
No additional documents have been identified.
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