Earlier Breast Cancer Detection Using Automated Whole Breast Ultrasound With Mammography, Including Cost Comparisons
Recruitment status was Recruiting
| Tracking Information | |||||
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| First Received Date ICMJE | February 25, 2008 | ||||
| Last Updated Date | March 27, 2008 | ||||
| Start Date ICMJE | January 2003 | ||||
| Estimated Primary Completion Date | July 2008 (final data collection date for primary outcome measure) | ||||
| Current Primary Outcome Measures ICMJE |
Numbers of breast cancers detected [ Time Frame: One year after sonocine screening ] [ Designated as safety issue: No ] | ||||
| Original Primary Outcome Measures ICMJE | Same as current | ||||
| Change History | Complete list of historical versions of study NCT00649337 on ClinicalTrials.gov Archive Site | ||||
| Current Secondary Outcome Measures ICMJE | Not Provided | ||||
| Original Secondary Outcome Measures ICMJE | Not Provided | ||||
| Current Other Outcome Measures ICMJE | Not Provided | ||||
| Original Other Outcome Measures ICMJE | Not Provided | ||||
| Descriptive Information | |||||
| Brief Title ICMJE | Earlier Breast Cancer Detection Using Automated Whole Breast Ultrasound With Mammography, Including Cost Comparisons | ||||
| Official Title ICMJE | Earlier Breast Cancer Detection Using Automated Whole Breast Ultrasound With Screening Mammography, Including Cost Comparisons | ||||
| Brief Summary | The purpose of this study is to determine whether addition of automated whole breast ultrasound to the usual screening mammography in a population of asymptomatic women with mammographically dense breasts will result in a significantly greater number of breast cancers discovered than would be found by mammography alone. |
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| Detailed Description |
Since the 16,000 patients mostly will be covering the costs of their own tests, it is anticipated that they will be coming from a well-screened population. Such a population over the age of 40 years would be expected to generate interval cancers at a rate of about 0.25% annually. Consequently, about 40 carcinomas would present themselves for discovery during the study. Given the 20 to 25% expected false negative rate from screening mammography 8 to 10 cancers will be available for discovery only by SonoCiné. If SonoCiné were to have an independent 20 to 25% false negative rate it would be expected to find 6 to 8 of the remaining cancers. If mammography is utilized alone with an estimated 75% accuracy, it is anticipated that 30 cancers would be identified based on mammography screening with a failure of 10 cancers not found. With the addition of Sonociné screening, it is predicted that additional 8 cancers would be found and the total failure rate (false negative findings) would be 2 cancers. This would increase the accuracy to 95%. If SonoCiné generally finds cancers at a smaller size than screening mammography, the actual number of cancers discovered by SonoCiné may be higher, since it will find some of the cancers that would be discovered by screening mammography the following year before they presented clinically. Also women with a known higher risk of breast cancer may disproportionately volunteer for this study and more cancers may be found both by mammography and automated whole breast ultrasound than expected. Since women are aware that mammographically dense breasts are more prone to be falsely negative by mammography, more women with this condition may join the study than expected. This may produce more mammographically occult cancers than expected. Breast density is one of the variables recorded in all subjects. Discriminant Function analysis will be the analysis of choice based on the fact that we wish to distinguish among several mutually exclusive groups, the best predictor that are important for distinguishing among the groups, and to develop a procedure for predicting group membership for new cases. The concept underlying discriminant analysis is that that linear combinations of independent variables are formed and serve as a basis for classifying cases into one of the groups. Assumptions include that each group must be a sample from a multivariate normal population and the population covariance matrices must be equal, although discriminant function analysis works fairly well in cases were there are exceptions. Dichotomous variables can also be included as predictor variables. Emphasis is on analyzing all the variables at one time and considering them together. By considering them simultaneously we are able to incorporate important information about their relationships with each other. Because the variables are interrelated, we will need to employ statistical techniques that incorporate these dependencies by analyzing the differences between groups by significance tests for the equality of group means for each variable utilizing F values, and their significance, and Wilks' Lambda to compare within group variability with total variability. Small values of lambda indicate that means associated with variables predicting group membership are different and may lead to model development. Since interdependencies among the variables affect most multivariate analyses, it is important to look at the correlation matrix of the predictor variables. Prior probability is an estimate of the likelihood that a case belongs to a particular group. Knowledge of prior probabilities can be calculated based on published statistics and is estimated to be .25% for cancer in the screening population. To take advantage of additional information available for developing a classification scheme for probability of group membership, classification of actual group membership can be compared with predicted group membership as well using discriminant function. Variables used to predict group memberships will be drawn from the Patient Form, the Imaging Form and the Biopsy Form. Variables will include initial risk factors, results of mammography findings, SonoCiné findings and the results of the biopsy. Although some variables are coded as categorical, most are ordinal and as interval and are appropriate for Discriminant Function analysis or the use of General Linear Model Procedure (GLM).Additional analysis looking at the distribution of time between events utilizing Life Tables and an extended Cox Regression model. Efficiency is defined as the use of resources that will produce the maximum benefit. Cost-benefit analysis can be performed at the end of the study by expressing both the benefits and costs of a program, not only in dollars but in quality of life and reduction of suffering. Benefits of the study, in addition to increased detection rate, may include over time an earlier detection of smaller cancers and an actual reduction in the need for biopsy. This will impact treatment and resource utilization also. |
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| Study Type ICMJE | Interventional | ||||
| Study Phase | Not Provided | ||||
| Study Design ICMJE | Intervention Model: Single Group Assignment Masking: Open Label Primary Purpose: Screening |
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| Condition ICMJE |
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| Intervention ICMJE | Procedure: Comparison of Automated Whole Breast Ultrasound Screening with Blinded Screening Mammography
AWBU is a computer-based system for performing and recording ultrasound scans of the whole breast. The transducer of any suitable high-resolution compound ultrasound equipment is attached to a mechanical arm guided by computer, and images are acquired in longitudinal rows, overlapping to assure complete coverage. The mechanical arm controls transducer speed and position, with a technician maintaining appropriate contact pressure and orientation vertical to the skin. Approximately 150-300 images per row are immediately displayed on the AWBU monitor, then permanently stored. The AWBU software creates a continuous ciné loop of the images, creating the appearance of real-time scanning. With spatial registration, any point on an image can be identified as a distance from the nipple in a specific radius. Image review is optimized by playback on a high-resolution monitor to allow compressed image size, 3-D reconstruction, and adjustment of contrast, brightness and review speed. Other Names:
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| Study Arm (s) | Experimental: 1
Adjunct screening with sonocine
Intervention: Procedure: Comparison of Automated Whole Breast Ultrasound Screening with Blinded Screening Mammography |
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| Publications * | Kelly KM, Dean J, Comulada WS, Lee SJ. Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol. 2010 Mar;20(3):734-42. Epub 2009 Sep 2. | ||||
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* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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| Recruitment Information | |||||
| Recruitment Status ICMJE | Recruiting | ||||
| Estimated Enrollment ICMJE | 4650 | ||||
| Estimated Completion Date | January 2010 | ||||
| Estimated Primary Completion Date | July 2008 (final data collection date for primary outcome measure) | ||||
| Eligibility Criteria ICMJE | Inclusion Criteria:
Exclusion Criteria:
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| Gender | Female | ||||
| Ages | 35 Years to 90 Years | ||||
| Accepts Healthy Volunteers | Yes | ||||
| Contacts ICMJE |
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| Location Countries ICMJE | United States | ||||
| Administrative Information | |||||
| NCT Number ICMJE | NCT00649337 | ||||
| Other Study ID Numbers ICMJE | SC0001, SC0001 | ||||
| Has Data Monitoring Committee | Yes | ||||
| Responsible Party | Kevin M. Kelly, M.D., SonoCine, Inc. | ||||
| Study Sponsor ICMJE | SonoCine, Inc. | ||||
| Collaborators ICMJE | Not Provided | ||||
| Investigators ICMJE |
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| Information Provided By | SonoCine, Inc. | ||||
| Verification Date | March 2008 | ||||
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ICMJE Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP |
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