Clinical trial NCT00649337: Earlier Breast Cancer Detection Using Automated Whole Breast Ultrasound With Mammography, Including Cost Comparisons
Official title: Earlier Breast Cancer Detection Using Automated Whole Breast Ultrasound With Screening Mammography, Including Cost Comparisons
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Breast Neoplasms
Ultrasonography
Keywords: mammography
ultrasound
breast cancer
screening
Lead sponsor: SonoCine, Inc.
Sponsor class: INDUSTRY
First posted: 2008-04-01
Enrollment: 4650
Sex: FEMALE
Minimum age (years): 35
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: SCREENING

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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* be at least 35 years old, unless she has a strong pre-menstrual family history of breast cancer
* have had no screening mammogram in the past 10 months
* agree to have a screening mammogram that will not be reviewed until after a SonoCiné is performed
* agree to have a screening mammogram in approximately one year
* agree to answer follow up question in approximately one year
* meet the usual criteria for breast screening (be asymptomatic of focal breast disease)
* have no history of breast cancer for at least one year
* agree to have both mammogram and SonoCiné if a physical finding or mammographic finding occurs within one year that requires biopsy

Exclusion Criteria:

* greater than 7 cm compressed breast thickness at mammography

Primary outcomes:
measure: Numbers of breast cancers detected
timeFrame: One year after sonocine screening
Secondary outcomes:
Other outcomes:
Locations:
city: Pasadena
country: United States
facility: Huntington Memorial Hospital/Hill Breast Center
state: California
Study officials:
affiliation: SonoCine, Inc.
name: Kevin M. Kelly, M.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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. doi: 10.1007/s00330-009-1588-y. Epub 2009 Sep 2.