This randomized phase III trial studies digital tomosynthesis mammography and digital mammography in screening patients for breast cancer. Screening for breast cancer with tomosynthesis mammography may be superior to digital mammography for breast cancer screening and may help reduce the need for additional imaging or treatment.
PRIMARY OBJECTIVES: I. To compare the proportions of participants in the tomosynthesis mammography (TM) and digital mammography (DM) study arms experiencing the occurrence of an ?advanced? breast cancer at any time during a period of 4.5 years from randomization, including the period of active screening and a period of clinical follow-up after the last screen (T4). SECONDARY OBJECTIVES: I. To assess the potential effect of age, menopausal and hormonal status, breast density, and family cancer history on the primary endpoint difference between the two arms. II. To compare the diagnostic performance of TM and DM, as measured by the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). III. To compare the recall rates and biopsy rates for TM versus DM, with subset analyses by the same variables as listed in aim II. IV. To compare the rate of interval cancers for TM and DM and to assess the mechanism of diagnosis for these interval cancers with categorization by symptomatic versus (vs) asymptomatic, and how detected: diagnosed via physical examination, mammography, ultrasound (US), magnetic resonance imaging (MRI) or other technologies. V. To examine the correlation between Breast Imaging Reporting and Data System (BIRADS) imaging features and histologic and genetic features, such as invasive ductal and invasive lobular histology, high grade, high stage at diagnosis, and aggressive genetic subtypes. VI. To assess different combinations of TM and synthesized 2 dimensional (2D) or DM in reader studies to assist in determining the optimum balance between diagnostic performance, radiation exposure and technique. VII. To estimate and compare breast-cancer-specific mortality between the two study arms. VIII. To estimate and compare the prevalence of breast cancer subtypes (luminal A, luminal B, HER2+, basal-like) low, medium or high proliferation via PAM50 proliferation signatures, and p53 mutant-like or wild-type-like according to a validated p53 dependent signature in the two arms, overall and stratified on whether cancers were detected through screening or as interval cancers, and whether cancers were invasive or in situ. IX. To classify histologically malignant (true positive cases) and benign lesions (false positive cases) as normal-like or tumor-like using the PAM50 gene expression assay subtype (luminal A, luminal B, HER2, basal-like,), and low, medium, or high proliferation according to PAM50 proliferation signatures, and p53 mutant-like or wild-type-like according to a validated p53-dependent signature. X. To assess the agreement between local and expert study pathologists for all breast lesions (benign and malignant) biopsied during the 4.5 years of screening with TM or DM. XI. To create a blood and buccal cell biobank for future biomarker and genetic testing. XII. To compare health care utilization (including cancer care received) and cost of an episode of breast cancer screening by TM versus DM, overall and within subsets. XIII. To implement a centralized quality control (QC) monitoring program for both 2D digital mammography (DM) and tomosynthesis (TM), which provides rapid feedback on image quality, using quantitative tools, taking advantage of the automated analysis of digital images. XIV. To assess temporal and site-to site variations in image quality, breast radiation dose, and other quality control parameters in TM vs. DM. XV. To refine and implement task-based measures of image quality to assess the effects of technical parameters, including machine type, and detector spatial and contrast resolution on measures of diagnostic accuracy for TM. XVI. To evaluate which QC tests are useful for determination of image quality and those that are predictive of device failure, in order to recommend an optimal QC program for TM. OUTLINE: Patients are randomized to 1 of 2 arms. ARM A: Patients undergo bilateral screening DM with standard craniocaudal (CC) and mediolateral oblique (MLO) views at baseline, 12, 24, 36, and 48 months if pre-menopausal or at baseline, 24, and 48 months if post-menopausal. ARM B: Patients undergo manufacturer-defined screening TM at baseline, 12, 24, 36, and 48 months if pre-menopausal or at baseline, 24, and 48 months if post-menopausal. After completion of study, patients are followed up for at least 3- 8 years after study entry.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SCREENING
Masking
NONE
Enrollment
108,508
Undergo DM
Undergo TM
Correlative studies
Proportion of women diagnosed with an advanced breast cancer at any time during a period of 4.5 years from randomization, including the period of active screening and a period of follow up after the last screen
The cumulative proportions of participants experiencing the primary endpoint in the two study arms will be compared. The primary comparison of the two study arms will be approached from an Intent-to-Treat perspective and will be based on a two-sided test for comparing binomial proportions.
Time frame: 4.5 years after last registration
Agreement between local and expert study pathologists for all breast lesions (benign and malignant) biopsied during the five years of screening
Measures of agreement such as kappa statistics and concordance coefficients to assess the agreement of local and central pathology readings. In addition, the variability among local pathologists will be examined with respect to the degree of agreement with the central study interpretation. This analysis will utilize mixed models with random effects for local pathologists. There will be up to two central study independent pathologist interpretations for each representative diagnostic slide set.
Time frame: Up to 8 years
Breast Imaging-Reporting and Data System (BIRADS) imaging features
The correlation between BIRADS imaging features and histologic and genetic features, such as invasive ductal and invasive lobular histology, high grade, high stage at diagnosis, and aggressive genetic subtypes will be examined. Using data on patients with cancer, estimates of the correlation between the two sets of features (BIRADS imaging features and histologic/genetic features) will be derived. Cluster analysis will be used to identify clusters of patients based on imaging features and will examine the association of these clusters with histology and genetic features. Using data from the fu
Time frame: Up to 8 years
Breast-cancer-specific mortality
Breast-cancer-specific mortality between the two study arms will be estimated and compared. Information on cancer recurrence and mortality will be obtained for a period of at least 4.5-8 years on all study participants. Mortality rates will be estimated as the ratio of the number of breast cancer deaths during a time period to the number of person-years at risk. Person-years will be measured as time from randomization to breast cancer death or censoring. Cumulative mortality rates from breast cancer at the end of the study period in each arm will be compared via the relative risk (rate ratio).
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University of Alabama at Birmingham Cancer Center
Birmingham, Alabama, United States
Mobile Infirmary Medical Center
Mobile, Alabama, United States
Banner-University Medical Center Phoenix
Phoenix, Arizona, United States
University of Arizona College of Medicine Phoenix
Phoenix, Arizona, United States
Valleywise Comprehensive Health Center - Phoenix
Phoenix, Arizona, United States
Mayo Clinic Hospital in Arizona
Phoenix, Arizona, United States
Scottsdale Medical Imaging Limited
Scottsdale, Arizona, United States
University of Arkansas for Medical Sciences
Little Rock, Arkansas, United States
Kern Radiology Medical Group Inc
Bakersfield, California, United States
Los Angeles General Medical Center
Los Angeles, California, United States
...and 139 more locations
Time frame: Up to 8 years
Centralized quality control (QC) monitoring program implementation
Centralized QC monitoring program for both DM and TM, which provides rapid feedback on image quality, using quantitative tools, taking advantage of the automated analysis of digital images. The QC program will provide an auditable trail of QC activities and image quality parameters, while at the same time reducing QC effort required by the technologist at the site. Constant monitoring of data from all sites will occur, and Root Cause Analysis will be performed for non-compliant items. The remote monitoring system will be evaluated in terms of its percent ?up-time?, technologist compliance (vi
Time frame: Up to 8 years
Diagnostic and predictive performance of tomosynthesis mammography (TM) and digital mammography (DM) [AUC]
ROC analysis will be performed to compare the performance characteristics of DM vs TM at each screening visit
Time frame: Up to 8 years
Assess the predictive performance of tomosynthesis mammography (TM) and digital mammography (DM)
Compare the predictive characteristics (PPV,NPV,Sens, and Spec) of DM vs TM at each screening visit
Time frame: Up to 8 years
Health care costs (including diagnostic procedures and cancer care received) as the result of an episode of breast cancer screening by tomosynthesis mammography (TM) versus digital mammography (DM)
Rates of utilization of key diagnostic procedures (e.g. extra TM or DM views, Ultrasound, Short-term interval follow-up, surgical consultation, percutaneous biopsy with, needle-localized open surgical biopsy, breast MRI) will be estimated; Medicare reimbursement costs will be used to derive a standardized measure of cost per participant; and these costs will be compared across the two study arms. These measures of cost will be compared across study arms using non-parametric methods.
Time frame: Up to 8 years
Health care utilization (including cancer care received) of an episode of breast cancer screening by tomosynthesis mammography (TM) versus digital mammography (DM)
Rates of utilization of key diagnostic procedures (e.g. extra TM or DM views, Ultrasound, Short-term interval follow-up, surgical consultation, percutaneous biopsy with, needle-localized open surgical biopsy, breast MRI) will be estimated and compared across the two study arms. The comparisons will be made using regression modeling
Time frame: Up to 8 years
Histologically malignant (true positive cases) and benign lesions (false positive cases)
Classification of histologically benign-appearing lesions (false positive cases) will be explored as normal-like or tumor-like using the PAM50 gene expression assay subtype and low, medium, or high proliferation according to a PAM50 proliferation signature, and p53 mutant-like or wild-type-like according to a validated p53-dependent signature, and according to histological features. The benign-appearing (false positive) biopsies will be characterized using digital histologic analysis tools that capture percent of area represented by stroma, epithelium, and fat as well as the density of nuclei
Time frame: Up to 8 years
Prevalence of breast cancer subtypes (luminal A, luminal B, HER2+, basal-like) and p53 signature in the two arms
Prevalence of breast cancer subtypes (luminal A, luminal B, HER2+, basal-like) and p53 signature in the two arms will be estimated and compared, overall and stratified on whether cancers were detected through screening or as interval cancers. subtypes in each arm and to compare them across arms. The analysis will be performed overall, and stratified by screen detected or interval detected. Estimates of the prevalence of subtypes and confidence intervals will be developed for each screening round and for the full period of screening. The comparison of rates across arms will be based on multinom
Time frame: Up to 8 years
Proportion of women diagnosed with an ?advanced? breast cancer in the two arms
The potential effect of age, menopausal and hormonal status, breast density, and family cancer history will be assessed on the primary endpoint difference between the two arms. Regression modeling will be used to assess the effect of age, menopausal and hormonal status, breast density, and family cancer history. Multiple imputation will be used to handle missing data in the response and the covariates, and a sensitivity analysis to assumptions about the missing data will be performed. An exploratory analysis using alternative definitions of the primary endpoint will also be conducted in order
Time frame: Up to 8 years
Quality control (QC) tests useful for determination of image quality and those that are predictive of device failure
QC tests that are useful for determination of image quality and those that are predictive of device failure will be evaluated, in order to recommend an optimal QC program for TM. Tests that are most sensitive to changes in system performance will be established and tests that are inferior and/or redundant and can be eliminated. Changes will be tracked against site records of alterations or repairs to the system, recalibration and changes in imaging parameters. Changes in test results will be observed and if they are suggestive that remedial action is required, we will determine after such acti
Time frame: Up to 8 years
Rate of interval cancers
The rate of interval cancers for TM and DM will be compared and the mechanism of diagnosis for these interval cancers will be assessed with categorization by symptomatic vs asymptomatic, and how detected: diagnosed via physical examination, mammography, ultrasound (US), magnetic resonance imaging (MRI) or other technologies. Interval cancers are those that occur between screening examinations. Interval cancer rates for each screening occasion and over the full set of screens will be estimated using Wilson intervals and compared across arms using two-sided tests for binomial proportions. The di
Time frame: Up to 8 years
Recall rates
The recall rates for TM versus (vs) DM will be compared. Recall rates are defined as the number of screening examinations that are interpreted as BIRADS 0, 3, 4 and 5 divided by the total number of screening examinations. Recall rates for each screening occasion and over the full set of screens will be estimated using Wilson intervals and compared across arms using two-sided tests for binomial proportions. Logistic regression will be used to analyze potential differences across patient subsets.
Time frame: Up to 8 years
Biopsy rates
The biopsy rates for TM versus (vs) DM will be compared. biopsy rates are defined as the number of biopsies divided by the total number of screening examinations. rates for each screening occasion and over the full set of screens will be estimated using Wilson intervals and compared across arms using two-sided tests for binomial proportions. Logistic regression will be used to analyze potential differences across patient subsets
Time frame: Up to 8 years
Task-based measure of image quality
Task-based measures of image quality will be refined and implemented to assess the effects of technical parameters, including machine type, and detector spatial and contrast resolution on diagnostic accuracy for TM. the diagnostic accuracy of the resulting Task-based analysis, using mathematical observers, will be assessed from image information using techniques based on signal and noise transfer.
Time frame: Up to 8 years
Variability of quality control parameters
The variability of standard quality control parameters will be assessed and compared temporally, within, and across sites for both DM and TM.
Time frame: Up to 8 years