Technology | Breast Imaging | May 20, 2016

FDA Approves Standalone 3-D Screening With Siemens Tomosynthesis Platform

Mammomat Inspiration with Tomosynthesis Option showed superior results to 2-D alone in reader study

Siemens, FDA, mammography, 3-D screening, tomosynthesis, Mammomat Inspiration

May 20, 2016 — Siemens Healthineers announced that the U.S. Food and Drug Administration (FDA) has approved the use of 3-D-only screening mammography utilizing the company’s Mammomat Inspiration with Tomosynthesis Option digital mammography system.

Siemens said the system is the first and only 3-D digital breast tomosynthesis (DBT) platform to be approved by the FDA as a stand-alone screening and diagnostic system; all other mammography systems on the market require a combination of 2-D and 3-D examinations.

FDA approval of the 3-D-only application follows a pivotal reader study in which participating radiologists demonstrated their ability to increase cancer detection at a lower radiation dose than combined 2-D and DBT. In the study, radiologists decreased average recall rates by an average of 19 percent without the need for a 2-D image.

Siemens’ Tomosynthesis Only Option is available on the company’s Mammomat Inspiration and Mammomat Inspiration Prime Edition digital mammography systems.

For more information: www.healthcare.siemens.com

Related Content

Cianna Medical featured its wire-free marker system on the exhibit floor of the breast imaging symposium in Hollywood, Fla.

Cianna Medical featured its wire-free marker system on the exhibit floor of the breast imaging symposium in Hollywood, Fla.

Feature | Breast Imaging | April 24, 2019 | By Greg Freiherr
Wires have traditionally been placed prior to lumpectomy to mark cancerous tissues in the breast.
GE Healthcare showcases Senographe Pristina with its add-on-biopsy kit at the breast imaging symposium

GE Healthcare showcases Senographe Pristina with its add-on-biopsy kit at the breast imaging symposium in Hollywood, Florida. Photo by Greg Freiherr

Feature | Breast Imaging | April 23, 2019 | By Greg Freiherr
Signs of what the future may look like in women’s health dotted the exhibit floor of the...
In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images

In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images. Photo by Greg Freiherr

Feature | Artificial Intelligence | April 19, 2019 | By Greg Freiherr
Commercial efforts to develop...
Videos | Breast Imaging | April 18, 2019
In a keynote lecture at the Society of Breast Imaging (SBI)/American College of Radiology (ACR) 2019 Symposium, ...
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...
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.