News | Mammography | October 16, 2017

TMIST Mammography Study Opens Enrollment

Study will compare 2-D and 3-D mammography to help women make informed decisions about screening tests in the future

TMIST Mammography Study Opens Enrollment

October 16, 2017 — The Tomosynthesis Mammographic Imaging Screening Trial (TMIST), the first randomized trial to compare two types of digital mammography for breast cancer screening, is now open for enrollment. The study was developed by the ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) and the National Cancer Institute (NCI), part of the National Institutes of Health. ECOG-ACRIN is leading the trial.

TMIST researchers are enrolling healthy women ages 45 to 74 who are already planning to get routine mammograms. By taking part in TMIST, the 165,000 planned participants will provide critical information that will help researchers learn how to most effectively screen women for breast cancer and help women make informed decisions about the screening tests in the future. 

“Nearly 50 million screening mammograms occur each year in the United States, yet it has been decades since a large-scale randomized trial of mammography has been done,” said Worta McCaskill-Stevens, M.D., director of the NCI Community Oncology Research Program (NCORP), the NCI program supporting the trial. “The evolution of mammography technology provides us with an opportunity to fill in the gaps in our knowledge about two available breast cancer screening tests.”

TMIST is comparing two types of digital mammography approved by the U.S. Food and Drug Administration (FDA) : tomosynthesis (known as three-dimensional, or 3-D) and conventional (two-dimensional, or 2-D). Although 3-D mammography, being the newer technology, is likely to detect more findings that require follow-up, it is also likely to lead to more procedures and treatments. It is not known if this newer mammography technology is reducing a woman’s risk of developing a life-threatening (advanced) cancer compared with 2-D mammography. The TMIST trial aims to find out.

“We need to determine if 3-D mammography is better than 2-D at finding the sort of breast cancers that are most likely to spread and kill women,” said ECOG-ACRIN study chair Etta D. Pisano, M.D., vice chair of research in the Department of Radiology at Beth Israel Deaconess Medical Center and professor in residence of radiology at Harvard Medical School, Boston. “If a newer screening technology does not reduce the numbers of advanced, life-threatening cancers, then are we really improving screening for breast cancer?”

TMIST researchers are collecting data on the results of every mammogram, whether the breast imaging shows no signs of cancer, findings suspicious of cancer, or a breast cancer. Any medical follow-ups, such as more imaging or biopsies, are also being reported. TMIST researchers intend to follow all participants for breast cancer status, treatment and outcomes from the time of randomization until the end of the clinical study (at least 2025).

Based on the findings of earlier studies, researchers know that the vast majority of women in the study will not develop breast cancer. If a woman does receive a diagnosis of any kind of breast cancer while in the trial, she will receive treatment just as she would if she was not part of TMIST, while continuing to be part of the trial.

In addition to data from mammograms, the trial is building a biorepository for future research on genetic markers for breast cancer by asking all participants to voluntarily submit blood samples and swabs of cells from inside the mouth (buccal cells). This data could, in the future, help women and their doctors decide the best ways to screen for breast cancer by evaluating their individual risk factors for developing the disease. TMIST researchers are also analyzing tissue collected from women who have biopsies during the trial because of mammogram findings that require follow-up. This is to learn more about the biology of breast cancers detected through screening.

About 100 mammography clinics in the United States are planning to participate in the trial and are opening on a rolling basis over the next several months. Women are being told about the opportunity to enroll in the trial when they schedule a routine mammogram. Once enrolled, they will be assigned to either 2-D or 3-D mammography screening. Most women enrolled in the trial will be screened annually. Postmenopausal women with no high-risk factors will be screened every two years.

To ensure a diverse group of participants, sites are well represented both geographically and by the race/ethnicity of the women the sites serve. Several Canadian clinics are joining the trial, having already enrolled more than 3,000 women in a smaller lead-in study that is helping to inform TMIST.

For more information: www.ecog-acrin.org

Related Content

Fatty tissue and breast density may be considered in the context of many factors that affect the occurrence and detection of breast cancer

Fatty tissue and breast density may be considered in the context of many factors that affect the occurrence and detection of breast cancer. Permission to publish provided by DenseBreast-info.org

Feature | Breast Imaging | April 18, 2019 | By Greg Freiherr
When planning a screening program to detect the early signs of breast cancer, age is a major consideration.
iCAD Appoints Stacey Stevens as President
News | Radiology Business | April 16, 2019
iCAD Inc. recently announced that Stacey Stevens has been named president. As president, Stevens will have expanded...
compressed breast during mammography.
360 Photos | 360 View Photos | April 16, 2019
A 360 view of a simulated breast compression for a...
Check-Cap Initiates U.S. Pilot Study of C-Scan for Colorectal Cancer Screening
News | Colonoscopy Systems | April 15, 2019
Check-Cap Ltd. has initiated its U.S. pilot study of the C-Scan system for prevention of colorectal cancer through...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used “Deep Learning,“ a form of machine learning, which is a type of artificial intelligence. Graphic courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Artificial Intelligence | April 12, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
This image depicts ABUS images with QVCAD results

This image depicts ABUS images with QVCAD results.

Feature | Breast Imaging | April 12, 2019
Imaging Technology News spoke with Bob Foley, vice president of sales and marketing of QView Medical, Inc.,
Deep Lens Closes Series A Financing for Digital AI Pathology Platform
News | Digital Pathology | April 09, 2019
Digital pathology company Deep Lens Inc. announced the closing of a $14 million Series A financing that will further...
Uterine Fibroid Embolization Safer and as Effective as Surgical Treatment
News | Interventional Radiology | April 05, 2019
Uterine fibroid embolization (UFE) effectively treats uterine fibroids with fewer post-procedure complications compared...