Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT00649337 |
| Other study ID # |
SC0001 |
| Secondary ID |
SC0001 |
| Status |
Recruiting |
| Phase |
N/A
|
| First received |
February 25, 2008 |
| Last updated |
March 27, 2008 |
| Start date |
January 2003 |
| Est. completion date |
January 2010 |
Study information
| Verified date |
March 2008 |
| Source |
SonoCine, Inc. |
| Contact |
Kevin M Kelly, M.D. |
| Phone |
626-793-6141 |
| Email |
dr.kelly[@]sonocine.com |
| Is FDA regulated |
No |
| Health authority |
United States: Institutional Review Board |
| Study type |
Interventional
|
Clinical Trial 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.
Description:
1. Study Design:
A total of sixteen thousand women at 16 centers, will be evaluated with SonoCiné
automated bilateral whole breast sonography and screening mammography. In this study a
motor driven carrier will be used to have the transducer examine each breast completely
[Appendix A]. In this way the images can be gathered in a contiguous manner and
displayed in a ciné strip and as three-dimensional images of each scan row.
These sonograms will be read by one of two designated investigator/radiologists at each
site. The mammograms will be not read until after the sonogram is performed and read,
and then will be read in the usual manner for that department without knowledge of the
automated breast ultrasound. The mammograms will not necessarily be read by one of the
investigators. The sonograms will initially be read and scored by one of the
investigators at each site. These sonograms will be reviewed as ciné loops and as
three-dimensional reconstructions of each scan row. The investigator will have no prior
knowledge of the patient's mammogram or the reading.
The data will be entered from each site into a central computer via internet. This
computer will have no personal information about any subject other than her institution
ID number and her birth date. Following entry of data from the separate readings of the
mammogram and sonogram, one investigator will assess any abnormal mammogram or sonogram
in light of the other imaging study for that patient to evaluate whether combined
readings of both studies would have effected patient management.
The investigators' call back, short-term follow-up and biopsy recommendation rates for
the mammography, the sonography and the combined reading will be recorded. As with
other breast imaging studies needle or open biopsies will be recommended for the
mammograms and/or sonograms that are read as suspicious for carcinoma. Further
diagnostic imaging studies will be ordered for any indeterminate finding.
2. Procedures for Patient Entry on Study:
Each patient is requested to obtain at her expense a screening mammogram on entry into
the study. She must also agree to have a screening mammogram one year later. The
patient must also give up the rights to the original films of the mammogram although
copies will be made for the patient without charge if needed. No other studies are
required. However the patient will also be required to fill out a demographic form.
3. Criteria for Response Assessment:
Each screening mammogram will be reviewed in the usual manner for that institution, not
necessarily by one of the investigators. Each screening ultrasound will be reviewed in
a timely manner by an investigator radiologist, and the patient will be informed of the
results either directly or through her physician. If any study is considered abnormal
the patient will be informed and called back for additional studies or other
appropriate action.
4. Definitions of Classification:
True and false positive and negative rates will be calculated based on the results of
the three initial imaging examinations, screening mammography (SM), screening automated
ultrasound (SU) and both screening studies combined (SC)), performed on each subject in
the PMA study. The above rates are calculated for each of the three initial imaging
examinations independent of the results of the other initial imaging examinations.
True Positive - A subject is TP for any of the three types of initial imaging
examinations (SM, SU, or SC) that leads to a biopsy proven cancer (invasive or in situ
breast cancer, or other intramammary malignancy).
True Negative - A subject is TN for each initial imaging examination whose initial
imaging examination is read as negative and who has a normal screening mammogram (or an
indeterminate mammogram subsequently shown not to be cancer) one year later, and has
not undergone a breast biopsy positive for cancer based on physical findings during the
interim.
False Positive - A subject is FP for a particular type of study, who has a benign
breast biopsy based on that study, and who does not have a breast cancer elsewhere
discovered by that study.
False Negative - A subject is FN for a particular type of initial imaging examination,
when the initial imaging examination is normal or benign, and a cancer is discovered on
one of the other types of initial imaging examinations, or is discovered on the
one-year mammogram, or is discovered by physical findings during the interim.
Callback - A callback (CB) occurs when a subject is recalled for further evaluation
because one of the initial imaging examinations is read as positive or indeterminate. A
SU callback is the sonographic equivalent of a diagnostic evaluation ordered from a SM
because of positive or indeterminate findings. Similarly, a SC callback occurs when
evaluation of both the SM and the SU together are indeterminate or positive and
requires further evaluation. CB rates will be calculated for each of the three initial
imaging examinations by comparing the number of CB's with the total number subjects. %
CB rate = Number of CB's / number of Study subjects.
Callbacks can have three different outcomes: resolution, by showing the initial imaging
examination was normal with further evaluation, short-term follow-up examination(s) at
a later date to evaluate the findings of the initial imaging examination and the CB
examination(s) further, or a biopsy because the initial imaging examination and/or the
call back examination were sufficiently suspicious to warrant tissue evaluation.
Follow-Up - Follow-up (FU) rates will be calculated for each of the three initial
imaging examinations by comparing the number of short-term follow-up's with the total
number of subjects. % FU rate = Number of FU's / number of Study subjects. The rate of
positive follow-ups will be calculated. % +FU = Number of +FU's / Total number of
follow-ups.
Biopsy - Biopsy rates will be calculated for each of the three initial imaging
examinations by comparing the number of biopsies with the total number of subjects. %
Biopsy rate = Number of Biopsies / number of Study subjects. The rate of positive
biopsies will be calculated. % + Biopsies = Number of + Biopsies / Total number of
Biopsies. A subject may have more than one biopsy.
5. "Off Study" Criteria:
This study requires the performance of an initial mammogram, an initial automated
screening ultrasound, and a final mammogram at one year. The patient also agrees to
allow access to the results of any further diagnostic tests that occur because of
positive findings in the original screening mammogram or ultrasound. The subject may
withdraw at any time, in which case her data will not be utilized in the analysis. Any
patient not completing the above tests will also be excluded from the analysis.
6. Statistical Considerations:
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.
