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Archive for the ‘FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning – GCP, GLP, ISO 14155’ Category

FDA Pending 510(k) for The Latest Cardiovascular Imaging Technology

Curator: Aviva Lev-Ari, PhD, RN

 

UPDATED on 11/22/2018

  • Device Approvals, Denials and Clearances

https://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/DeviceApprovalsandClearances/default.htm

  • FDA clears AI technology that evaluates echocardiograms – Ultrasound Images

https://www.healthdatamanagement.com/news/fda-clears-ai-technology-that-evaluates-echocardiograms

  • Heart Murmur Detection done by AI Algorithm (Eko Core and Eko Duo) Devices Outperform most Auscultatory Skills of Cardiologists

https://pharmaceuticalintelligence.com/2018/11/21/heart-murmur-detection-done-by-ai-algorithm-eko-core-and-eko-duo-devices-outperform-most-auscultatory-skills-of-cardiologists/

  • FDA Clears Remote Multichannel ECG Compared to Holter

https://www.cardiovascularbusiness.com/topics/electrophysiology-arrhythmia/fda-clears-remote-multichannel-ecg-compared-holter

  • Arterys Cardio AI – MR Images

Arterys CEO Fabien Beckers, along with Michael Poon, MD, Northwell Health cardiologist, will present “The Potential of a Web Platform to Transform Medical Imaging with AI and Cloud Computation” in the 2018 RSNA Machine Learning Showcase, Tuesday November 27 at 11:30am CST. Arterys will provide demonstrations of its AI-powered, web-based solutions, including:

Arterys Cardio AIMR combines the power of deep learning and cloud computing to automate analysis of cardiac MR images. By eliminating many tedious, manual tasks, Arterys Cardio AI enables clinicians to quickly and easily identify, determine treatment for and track heart problems. It is the first and only commercial solution to offer deep learning-based semi-quantitative perfusion and quantitative delayed enhancement analysis*.

https://www.marketwatch.com/press-release/arterys-to-demonstrate-suite-of-ai-powered-cloud-based-medical-image-analysis-solutions-at-rsna-2018-2018-11-21/print

  • AI software for detecting brain bleeds receives FDA approval – CT Images

The FDA recently administered 510(k) clearance to software developed by MaxQ AI that uses AI to detect brain bleeds on CT images, according to a report published Nov. 8 by AI in Healthcare.  

“The Accipio Ix Intracranial Hemorrhage platform uses AI technology to automatically analyze non-contrast head CT images, and can do so without impacting a physician’s workflow, altering the original series or storing protected health information,” according to the article.

The clinical diagnostics intelligence platform company hopes that the software can help physicians prioritizes patients who show symptoms of brain bleeds.

With FDA approval, the AI software can be sold for commercial use within the U.S. and will be on display during this year’s Radiological Society of North America (RSNA) Annual Meeting in Chicago.

https://www.healthimaging.com/topics/artificial-intelligence/ai-detection-software-brain-bleeds-fda-approved

 

  • More in Artificial Intelligence

SOURCE

https://www.healthimaging.com/topics/artificial-intelligence/ai-detection-software-brain-bleeds-fda-approv

Cardiovascular Medical Devices in the News

March 13, 2018 — Determining the best occluder device size necessary to properly seal the left atrial appendage (LAA) before implanting the device may be feasible with the assistance of 3D printing, according to two separate presentations at ECR 2018 in Vienna.

SOURCE

https://www.auntminnieeurope.com/index.aspx?sec=sup&sub=car&pno=2

  • Machine learning can help assess atherosclerosis
    February 7, 2018 — Machine-learning techniques analyze imaging measurements to automatically stratify patients by the level of atherosclerotic burden, offering the potential of personalized prediction of disease progression and more effective treatment for individual patients, according to researchers from Italy.  Discuss

SOURCE

https://www.auntminnieeurope.com/index.aspx?sec=sup&sub=car&pno=3

  • CCTA biomarker may predict mortality from heart disease
    August 28, 2018 — The use of coronary CT angiography (CCTA) to measure fatty tissue around arteries could help predict the risk of mortality from heart disease, according to research published online on 28 August in the Lancet and being presented at the European Society of Cardiology congress in Munich.  Discuss

SOURCE

https://www.auntminnieeurope.com/index.aspx?sec=sup&sub=car&pno=1

  • SCOT-HEART: CCTA cuts risk of heart attack, death by 41%
    August 25, 2018 — Patients with chest pain who underwent coronary CT angiography (CCTA) with standard care had a markedly lower rate of myocardial infarction or death from coronary artery disease than those who only received standard care in a new study, published on August 25 in theNew England Journal of Medicine.  Discuss

SOURCE

https://www.auntminnieeurope.com/index.aspx?sec=sup&sub=car&pno=1

 

 

SOURCE

https://www.healthdatamanagement.com/tag/cardiovascular-disease

FDA’s Medical Devices Frontier in 2013

Michelle McMurry-Heath

Office of the Center Director, Center for Devices and

Radiological Health, U.S. Food and Drug Administration (FDA)

and

Margaret A. Hamburg

Office of the Commissioner, FDA

In their article Creating a Space for Innovative Device Development stated that the FDA announces a partnership with a new nonprofit organization—the Medical Device Innovation Consortium (MDIC) —to advance regulatory science in the medical technology arena.

The promise of MDIC is to eliminate the currently existing shortfalls in applied research in areas such as health-related engineering and regulatory science, which comprises the development of new tools, standards, and approaches to assess a product’s safety, efficacy, quality, and performance.

MDIC will foster regulatory science breakthroughs in the medical technology space with the ultimate goal of improving human health.

FDA and LifeScience Alley (LSA; https://www.lifesciencealley.org)—a biomedical science trade association—have worked together to develop the first medical device public-private partnership (PPP) whose sole objective is to advance the entire spectrum of regulatory science in this sector. MDIC will facilitate this groundbreaking collaboration among federal agencies, nonprofit organizations, industry, academic institutions, and other trade associations such as MassMedic (www.massmedic.com) and the California Healthcare Institute (www.chi.org). Key goals:

(1) encourage members to leverage their resources by focusing jointly on precompetitive

(2) early-stage technology development ef orts that otherwise would not take place because of the organizational structure of the device sector.

About 75% of the more than 5,000 device manufacturers in the United States are small companies with fewer than 20 employees (3).

Start-up device companies have limited capital, and a startup’s future of en depends on the success of one complex device. Advances in regulatory science would speed the translation of these next-generation technologies.

Medical Devices sector lacks the resources to support regulatory science research, as well as mechanisms for working together to pool their resources to solve scientific issues.

MDIC members will make it a priority to develop regulatory methods and tools that can be adopted by the medical device community and will provide a forum for medical device stakeholders to securely share proprietary precompetitive data. Each advance achieved by medical device stakeholders through the sharing and leveraging of resources will assist industry in developing new REGULATORY SCIENCE Creating a Space for Innovative Device Development.

GOALS OF PARTNERING WITH MDIC

MDIC was designed with f exibility in mind, so that it can adapt to address the most pressing needs of patients and of the device industry as they evolve over time.

In keeping with the goal of stakeholder engagement, MDIC is currently recruiting founding members who will work jointly with FDA to determine research priorities for the endeavor.

Much like other successful PPPs in the pharmaceutical space, such as the Foundation for NIH or Critical Path Institute, the founding members will be asked to represent their stakeholder communities in

(i) suggesting the most promising areas for research collaboration,

(ii) raising funds to support these areas of investigation, and then

(iii) issuing requests for grant proposals.

Researchers and engineers from all sectors—industry, government, academia, or nonprofit organizations—will be encouraged to apply, and preference will be given to research consortia that cross sectors and take interdisciplinary approaches to problems.

MDIC strives to support science conducted by research teams that have innovative ideas for the development of tools and methods for medical device design, testing, and regulatory approval.

MDIC’s potential to improve patient care is computational modeling and simulation of human pathophysiology, which can be used to augment in vitro and animal disease models in the preclinical stages of device development.

FDA’s Center for Devices and Radiological Health (CDRH) expects computational modeling to accelerate and streamline the regulatory review process but first needs to develop a strategy for assessing the technology’s credibility—its usefulness, quality, and reproducibility. CDRH has begun to develop a technological framework called the Virtual Physiological Patient (4), which, once completed, will provide a model for the human body as a single complex system. 

However, cross-sector research teams are required to develop the normal and diseased reference models that will serve as benchmarks for device performance and safety. Using computational modeling and simulation, device designs can potentially be ref ned even before they enter clinical trials, improving safety for patients and reducing the cost of device development for companies, computational modeling and simulation, device designs can potentially be ref ned even before they enter clinical trials, improving safety for patients and reducing the cost of device development for companies.

Another emerging research area is medical device interoperability—the development of devices that seamlessly operate with other medical devices and information systems (5). MDIC could establish a framework to identify gaps in the interoperability field, prioritize the gaps, and then fund research accordingly.

MDIC also could help prioritize the development of standards for innovative interoperable medical devices and build test beds for these technologies. is research will help to ensure that interoperability issues do not pose a hazard to patients.

With the emergence of new materials in medical devices, FDA must develop updated biocompatibility standards based on the most recent scientific advances.

MDIC could support the development of new preclinical biocompatibility assays that predict potential adverse health responses in people exposed to biomaterials or nanoparticles (6).

INNOVATION INFRASTRUCTURE With today’s fiscal realities, FDA cannot rely on government-funded “Manhattan projects” to bridge the funding gap for regulatory science. Partnerships bring together private-sector expertise, academic science ingenuity, and federal regulatory knowledge, and new structures are needed to promote these multifaceted collaborations.

It would be convenient if such partnerships formed organically, but all too of en, bureaucratic red tape gets in the way of sensible scientif c collaboration. MDIC will serve as a collaborative freeway to biomedical discovery and development by forming a foundation that makes it easy for industry, academia, and government to come together to set research priorities; to pool their distinct intellectual capital; and then to work together to advance knowledge that modernizes regulatory science and improves patient access to high-quality medical technology.

Sci. Transl. Med. 4, 163fs43 (2012)

[ScienceTranslationalMedicine.org 5 December 2012 Vol 4 Issue 163 163fs43]

Statistics on Device use — Number of procedures in the United States (2009)

Number of domestic inpatient procedures (N = 48 million per year)

  • Insertion of coronary artery stent: 528,000
  • Diagnostic ultrasound: 902,000
  • CT scan: 497,000
  • Arteriography and angiocardiography: 1.9 million
  • Cardiac catheterization: 1.1 million
  • Total hip replacement: 327,000
  • Total knee replacement: 676,000

Source:

U.S. Centers of Disease Control www.cdc.gov/nchs/fastats/insurg.htm

This sector is best known for

  • surgical instruments,
  • cardiology devices, and
  • orthopedic implants, it also includes all of the
  • diagnostic tests and
  • imaging equipment currently used to pinpoint disease 
  • companion diagnostics, which are needed to fulfill the promise of personalized medicine (1).

FDA 510 (k) Pending for the Latest Cardiovascular Imaging Technology

Editor’s choice of the most innovative technology at RSNA 2012
By:

Dave Fornell

December 11, 2012
Toshiba is developing a radiation dose alert to show interventionalists how much dose they have delivered to their patient from X-ray angiography.
 The latest advances in cardiovascular imaging are usually shown first at the Radiological Society of North America (RSNA) annual meeting, the largest radiology show in the world, held the last week of November in Chicago. After spending five days walking three expo halls filled with more than 600 product vendors, the following is my editor’s choice for the most innovative new cardiovascular imaging technology.

New Angiography Systems

Siemens unveiled two new 510(k)-pending angiography systems, the Artis Q and Artis Q.zen, which incorporate new X-ray tube, detector and imaging software technology that can help reduce dose significantly, while offering improved image quality.

The new X-ray tube is intended to help physicians identify small vessels up to 70 percent better than conventional X-ray tube technology. The Artis Q.zen combines this innovative X-ray source with a new detector technology designed to support interventional imaging in ultra low-dose ranges to patients, doctors and medical staff, particularly during more complex, longer interventions.

The second generation of Siemens’ flat emitter technology replaced the coiled filaments used in conventional X-ray tubes to emit electrons. Flat emitters are designed to enable smaller quadratic focal spots that lead to improved visibility of small vessels.

The Artis Q.zen combines the X-ray tube with a detector technology that allows detection at ultra-low radiation levels. It can image with doses as low as half the standard levels applied in angiography. Instead of detectors based on amorphous silicon, a new crystalline silicon structure of the Artis Q.zen detector is designed to be more homogenous, allowing for more effective amplification of the signal, greatly reducing the electronic noise.

Siemens also introduced new software applications for interventional imaging. Clear Stent Live freezes an enhanced image of a stent during deployment with the balloon radio-opaque markers and uses it as an overlay on live fluoroscopy. Siemens says the main application will be for better visualization when implanting overlapping stents or stenting bifurcation lesions. It also helps suppress and stabilize heart motion on the image.

Other new 3-D applications are designed to image the smallest structures inside the head. Their high spatial resolution is crucial for imaging intracranial stents or other miniscule structures such as the cochlea in the inner ear. Moving organs such as the lungs can be imaged in 3-D in less than three seconds, reducing motion artifacts and the required amount of contrast agent.

GE Healthcare showcased its IGS (Image Guided System) 750 hybrid OR angiography system. It was displayed at RSNA 2011, but did not receive FDA clearance until earlier this year. It offers the mobility of a mobile C-arm, but the image quality and software features of a ceiling or floor mounted fixed system. It uses laser guidance for very accurate positioning. It can rove around the room on a powered caster system to enable different positioning around the table, or be parked out of the way during open surgical procedures.

Hands-Free Physician Control of Images

GestSure displayed a new, FDA-cleared system that allows interventionalists in the cath lab, or surgeons in the operating room, to pick reference images to display on the overhead screens in the room and manipulate the images all hands-free. It allows physicians to pick and enlarge the images they need for better procedural navigation, while maintaining the sterile field.

A video sensor detects all the people in the work area and displays their outlines on a separate screen, with each person assigned a specific color. When one of those people raises their arms in the “hands up” pose, the system detects this and allows the person control of the system. Using the right arm/hand, they can scroll through images and use the left arm/hand as a mouse click by a pushing motion forward. The system detects the motions and translates them in real time to mouse actions on the overhead screen.

The software works as a vendor-neutral layer on top of existing PACS or advanced visualization software.

Outpatient, Office-Based Catheter Interventions

Outpatient, office-based peripheral vascular procedures are an increasing trend, according to GE healthcare, which showcased a new “mobile hybrid OR” solution. The trend includes setting up an outpatient cath lab in an office setting to reduce the costs of using hospital ORs or cath labs. The room system GE highlighted centers around its OEC 9900 Elite mobile C-arm and Venue 40, which is combined with a ultrasound system in an all-in-one unit. The GE Venue 40 tablet ultrasound system is mounted within the OEC 9900 Elite C-arm’s workstation to reduce the floor space required.

Wireless Ultrasound Transducer

Siemens introduced the world’s first wireless transducer ultrasound system, the Acuson Freestyle. It eliminates the impediment of cables in ultrasound imaging by using a battery-powered transducer, about the size of a large TV controller. The transducer can be submerged for cleaning. It is capable of 90 minutes of continuous scanning before the battery needs to be recharged.

The Freestyle is a point-of-care system that will expand ultrasound’s use in interventional and therapeutic applications. The transducer can be used to image up to 10 feet from the console. Siemens said it hopes to refine and expand the wireless transducer technology to its other systems in the coming years.

Engineers had to overcome several issues to create a wireless transducer. For example, a cardiac echo requires about 40 frames per second and each frame is equal to about 1 megabyte of data. To accommodate the amount of data and speed the computer processing involved, some of the electronics are placed in the transducer rather than processing the data in the machine console. The wireless system transmits the data over an 8 GHz ultrawideband radio frequency to the console. The amount of data and the bandwidth transmitted by the transducer is equal to about 10 4G smart phones working continuously.

Noiseless MRI

GE Healthcare introduced its 510(k)-pending noiseless MRI Silent Scan technology that it hopes to introduce in 2013 for its MR450W 1.5T system. The technology addresses one of the most significant impediments to patient comfort — excessive noise generated during the exam that can be in excess of 110 decibels. A combination of software and a pulse sequence lowers the noise level to that of a chirping bird outside a window.

Historically, acoustic noise mitigation techniques have focused on insulating components and muffling sound as opposed to treating the noise at the source. With Silent Scan, acoustic noise is essentially eliminated by employing a new advanced 3-D acquisition and reconstruction technique called Silenz, in combination with GE Healthcare’s proprietary design of the high-fidelity MR gradient and RF system electronics. Silent Scan is designed to eliminate the noise at its source.

640-Slice CT Scanner

Toshiba unveiled its 640-slice Aquilion One Vision edition CT scanner. The vendor already offers the highest-slice system on the market, the 320-slice Aquilion One. The new system is equipped with a gantry rotation of 0.275 seconds, a 100 kw generator and 320 detector rows (640 unique slices) covering 16 cm in a single rotation, with the industry’s thinnest slices at 500 microns (0.5 mm). The system can accommodate larger patients with its 78 cm bore and fast rotation, including bariatric and patients with high heart rates.

FFR-Like CT Culprit Vessel Analysis

TeraRecon released new research software in response to fractional flow reserve (FFR)-CT analysis being developed by HeartFlow. The HeartFlow software uses a supercomputing algorithm to look at the fluid dynamics of the iodine contrast flow in coronary vessels to calculate a virtual a FFR number, similar to invasive pressure wire based FFR in the cath lab. TeraRecon’s Lesion Specific Analysis software cannot calculate FFR, but uses the same principle of tracking contrast flow in the myocardium. It uses lobular decomposition to look at each vessel segment to determine the tissue it feeds to show areas of ischemia and the expected culprit vessel segment. It shows a color contrast level maps on a 3-D model of the heart and in a coronal view of the left ventricle. Automated detection boxes highlight suspected ischemic areas of interest and identifies the vessel responsible for supplying blood to the region.

Radiation Dose Monitoring

Radiation dose monitoring solutions have been shown at previous RSNAs, but were highlighted by several companies this year as several states began implementing requirements for radiology departments to record patient dose. Dose records will have the most application with CT systems, especially for longer duration, higher dose cardiac exams, and catheter based angiography. Angiography is becoming an increasing issue due to the longer duration of more complex transcatheter interventions.

Toshiba demonstrated a work-in-progress dose tracking software for its Infinix-i angiography system. It can be displayed on a screen in the cath lab to show the approximate radiation dose that has been delivered cumulatively to specific areas of a patient. It takes into consideration the amount of time, power setting used and orientation of the C-arm to show a color-coded map of radiation delivery projected on a human figure. The colors change in real time as X-ray imaging continues. It is designed to be a visual reminder to physicians about the dose the patient has received and that they may want to change the location of the C-arm.

Sectra demonstrated 510(k)-pending Dose Track software, which radiology or cardiology departments can use to track radiation dose by patient, machine, physician, technologist, procedure type and room. The system can be set up to create alerts if a reasonable amount of dose if exceeded for a particular exam, or if certain physicians or technologists are using higher than average doses.

OLED Displays

Flat panel display technology migrated from CRT screens to LCDs over the past decade. The next major innovation in display technology is OLED, which offers even smaller components, faster response time than LCD, and the ability to display quick motion with virtually no blur. Sony showed the new PVM-2551MD OLED medical-grade monitor, which incorporates technology to achieve pure black, faithful to the source signal. By providing superb color reproduction, especially for dark images, surgeons can observe very subtle details such as the faint color difference between various tissues and blood vessels.

Aesthetically Pleasing Cath Labs

Philips Healthcare displayed video of its recent install of the Ambient Experience in a cath lab. The system uses colored lighting, subtle room design details and projected image visual effects to calm patients and make procedure rooms look less clinical. The installation highlighted allowed doctors or patients to choose a theme, such as a tropical rainforest, where diffused, indirect lighting would take a green hue and a photo projection on the ceiling of a tropical scene. Philips said at facilities that have installed these type of labs, patient satisfaction rose, as did staff morale. They say doctors and staff compete to use these rooms at some facilities.

Single Detector Spectral CT Imaging

Philips introduced an innovative work-in-progress CT system that uses new detector technology to simplify spectral imaging, offering soft tissue image quality similar to MRI. Currently, CT special imaging can be performed using systems with two X-ray tubes and two detectors. The new system in development uses a single X-ray source and a single detector that has two layers of detectors, one on top of the other, for high and low energy.

Better Transcatheter Mitral Valve Repair Guidance

Philips’ showed its new Echo Navigator system, designed to synchronize views from TEE ultrasound with the orientation on live angiography. The primary application is to aid navigation during transcatheter mitral valve procedures, which require very accurate 3-D echo navigation to deploy devices like the Abbott MitraClip.

3-D Sculptures From 3-D Datasets

Taking 3-D images shown on 2-D display screens to a true physical 3-D form, Vidar Systems/3D Systems displayed the new Z Printer 450. It takes any 3-D advanced visualization dataset and can print the image in true 3-D using gypsum powder (the same material used to make drywall), standard color ink jet printer cartridges and a binding agent. The image is saved as an STL file and sent to the printer, which prints 1/10th of a millimeter each pass, up to 2 cm per hour.

The 3-D sculptures it created can be printed in color, eliminating the need to paint the models.

The printer offers a new way to create 3-D anatomical models for medical education, complex surgical planning and cosmetic reconstruction. Another application suggested at RSNA was to print sculptures for sale to the patients, such as fetal faces taken from 3-D obstetrics ultrasound exams.

The company printed a full-sized, 3-D, color heart during the show using a cardiac CT dataset on a thumb drive provided by one of the advanced visualization vendors in the same hall.

  • Siemens unveiled the world’s first wireless ultrasound transducer at RSNA 2012.

http://www.dicardiology.com/article/latest-cardiovascular-imaging-technology

REFERENCES

1. S. Desmond-Hellmann, Toward precision medicine: A new

social contract? Sci. Transl. Med. 4, ed3 (2012).

2. J. S. Altshuler, E. Balogh, A. D. Barker, S. L. Eck, S. H. Friend,

G. S. Ginsburg, R. S. Herbst, S. J. Nass, C. M. Streeter, J. A.

Wagner, Opening up to precompetitive collaboration. Sci.

Transl. Med. 2, 52cm26 (2010).

3. U.S. commerce department study; www.ita.doc.gov/td/

health/Medical%20Device%20Industry%20Assessment%

20FINAL%20II%203-24-10.pdf.

4. Regulatory science in FDA’s Center for Devices and

Radiological Health: A vital framework for protecting

and promoting public healthwww.fda.gov/AboutFDA/

CentersOffices/OfficeofMedicalProductsandTobacco/

CDRH/CDRHReports/ucm274152.htm#.

5. Driving Biomedical Innovation: Initiatives for Improving

Products for Patients; www.fda.gov/AboutFDA/

ReportsManualsForms/Reports/ucm274333.htm.

6. G. D. Prestwich, S. Bhatia, C. K. Breuer, S. L. Dahl, C. Mason,

R. McFarland, D. J. McQuillan, J. Sackner-Bernstein, J. Schox,

W. E. Tente, A. Trounson, What is the greatest regulatory

challenge in the translation of biomaterials to the clinic?

Sci. Transl. Med. 4, 60cm14 (2012).

7. Between Invention and Innovation. NIST GRC 02-841;

www.atp.nist.gov/eao/gcr02-841/contents.htm.

8. Justin D Pearlman, MD, ME, PhD, FACC, MA; Chief Editor: Eugene C Lin, MD

Imaging in Coronary Artery Disease, Nov 13, 2012

http://emedicine.medscape.com/article/349040-overview

9. Markus Schwaiger, MD; Sibylle Ziegler, PhD; and Stephan G. Nekolla, PhD

PET/CT: Challenge for Nuclear Cardiology

THE JOURNAL OF NUCLEAR MEDICINE • Vol. 46 • No. 10 • October 2005

 

Other articles related to this topic Published on this Open Access Online Scientific Journal include the following:

New Definition of MI Unveiled, Fractional Flow Reserve (FFR) CT for Tagging Ischemia

http://pharmaceuticalintelligence.com/2012/08/27/new-definition-of-mi-unveiled-fractional-flow-reserve-ffrct-for-tagging-ischemia/

FDA: Strengthening Our National System for Medical Device Post-market Surveillance

http://pharmaceuticalintelligence.com/2012/09/07/fda-strengthening-our-national-system-for-medical-device-post-market-surveillance/

Gaps, Tensions, and Conflicts in the FDA Approval Process: Implications for Clinical Practice

http://pharmaceuticalintelligence.com/2012/07/31/gaps-tensions-and-conflicts-in-the-fda-approval-process-implications-for-clinical-practice/

To Stent or Not? A Critical Decision

http://pharmaceuticalintelligence.com/2012/10/23/to-stent-or-not-a-critical-decision/

Read Full Post »

Automated Breast Ultrasound System (‘ABUS’) for full breast scanning: The beginning of structuring a solution for an acute need!

Writer: Dror Nir, PhD

Screen Shot 2021-07-19 at 7.26.58 PM

Word Cloud By Danielle Smolyar

GE Healthcare announced this week the acquisition of U-Systems, Inc. U-systems has developed the first and only Automated Breast Ultrasound System (ABUS) on the market – somo•v®, to receive FDA approval as an adjunct to mammography screening for breast cancer of; “asymptomatic women, with greater than 50 percent dense breast tissue and no prior breast interventions.”

somo•v® screen shot, showing mass in upper-outer quadrant of the left breast. Image courtesy of U-Systems.

I became aware of somo•v® already in 2004, when Prof. André Grivegnée, head of the breast screening unit at Jules Bordet – European oncology center in Brussels, Belgium, invited me to participate in a technology assessment of U-Systems’ somo•v® product. On that occasion, I also shared with U-System’s developers the idea of incorporating tissue characterisation into their product, an idea which they did not take on board. There is nothing more vivid to fully understand the meaning of this acquisition for breast cancer screening then the following quote from AuntMinnie’s report “GE taps interest in ABUS with U-Systems acquisition”:  “You know you’re onto something when the big boys come calling. GE Healthcare today announced its acquisition of automated breast ultrasound (ABUS) developer U-Systems, a move that highlights the rapid evolution of ABUS from a niche technology into a promising adjunct to screening mammography. “ First savvy: The reality of medical device startups is that it doesn’t matter how real and large is the need for your technology. Until one of the big boys will adopt it, it is prone to be considered as niche technology. I discussed the potential role of ABUS in future breast screening in my recent posts: Closing the Mammography gap; Introducing smart-imaging into radiologists’ daily practice.  As noted, in recent years, several ABUS systems were developed. An intriguing question is; why did GE choose to buy the somo•v® and not one of the other systems? Why now and not 2 or 3 years ago? The answer must have to do with the fact that in September 2012, somo•v® became the first ABUS system to receive premarket approval (PMA) for its application to use the system in a breast cancer screening environment. Until then, somo·v was indicated for use as an adjunct to mammography for B-mode ultrasonic imaging of a patient’s breast when used with an automatic scanning linear array transducer or a handheld transducer. The PMA has extended somo·v’s Indication For Use (IFU) allowing a claim that it increases breast cancer detection in a certain patients population. Second savvy: Having a PMA approval for a compelling indication for use, in a significant enough patient group, will dramatically increase “big boys” interest in your product. From the information available on the FDA site, one can get an insight into U-System’s regulatory strategy. They were smart enough to be satisfied with achieving a small step; increasing the detection rate of mammography-based screening. Therefore, the same radiologist who read the mammograms also read the ultrasound image. This increases the probability that your device’s sensitivity will not be worse than that of mammography. U-Systems did not try to go all the way to become an alternative to mammography. A claim that would significantly increase the complexity of the required clinical study; e.g. will require comparison of cancer detection-rates between modalities by independent, blinded-readers. Therefore, “the device is not intended to be used as a replacement for screening mammography”.   Third savvy: The most expensive component, in time and money, in a regulatory pathway are the clinical studies. A cost-effective regulatory strategy is linked to good understanding of the market segmentation. Identifying what kind of IFU differentiates your products from its competition in a large enough niche-market is key. It will also lead to the simplest clinical-study design possible. As an entrepreneur, I cannot help congratulating U-Systems’ team for pulling through continuous hurdles to reach the point all medical device startups are hoping for. They certainly picked up the right item to focus their efforts on: i.e. PMA approval for breast cancer screening. Finally, I will reiterate my vision that embedding real-time tissue characterization in an ultrasound system, capable of performing fast and standardized full breast scanning is: a. Technologically achievable; and b. in the long-term, will be an excellent alternative to mammography for breast cancer screening. Additional readings: Two studies related to  somo•v® will be discussed at the 2012 RSNA meeting: “ A study led by Dr. Rachel Brem of George Washington University Medical Center: ABUS plus mammography finds cancer early in women with dense tissue  Brem’s study found that ABUS enabled detection of early-stage cancers in women with dense breasts, giving healthcare providers time to start early treatment. In all, 88% of cancers found by ABUS alone in a group of 15,000 women were grade 1 or 2.” “A study presented by Maryellen Giger, PhD, of the University of Chicago: ABUS boosts mammography’s performance  this study results showthat adding ABUS to mammography for women with dense breast tissue improved sensitivity by 23.3 percentage points, from 38.8% for mammography alone to 63.1% for mammography plus ABUS.” As I mentioned already, there are other ultrasound modalities out there, some are ABUS and some are not. All are adjunct to mammography screening. Related studies will also be presented during that same meeting.

UPDATE (04-Aug-2013)

Here below is a recent publication on  the use of ABUS for better detection of breast cancer in patients presented with mammographically dense breast.

Improved breast cancer detection in asymptomatic women using 3D-automated breast ultrasound in mammographically dense breasts

  • Breast Cancer Research Institute, Nova Southeastern University College of Medicine, 5732 Canton Cove, Winter Springs, FL 32708, USA

Abstract

Automated breast ultrasound (ABUS)was performed in 3418 asymptomatic women with mammographically dense breasts. The addition of ABUS to mammography in women with greater than 50% breast density resulted in the detection of 12.3 per 1,000 breast cancers, compared to 4.6 per 1,000 by mammography alone. The mean tumor size was 14.3 mm and overall attributable risk of breast cancer was 19.92 (95% confidence level, 16.75 – 23.61) in our screened population. These preliminary results may justify the cost-benefit of implementing the judicious us of ABUS in conjunction with mammography in the dense breast screening population.

Keywords

  • Breast ultrasound;
  • 3-dimensional sonography;
  • Breast screening;
  • Dense breast;
  • Breast cancer;
  • Cancer detection

1. Introduction

Mammographic density as an independent risk factor for developing breast cancer has been documented since the 1970’s [1]. The appearance of breast tissue is variable among women. The appearance of density on mammography is the result of the relative proportion of breast stroma, which is less radiolucent compared to fat, accounting for increased breast density. Wolfe classified breast density as an independent risk factor for breast cancer in women [2] and [3]. Approximately 70 to 80% of breast cancers occur in women with no major predictors [4][5] and [6]. Population-based screening for early detection of breast cancer is therefore the primary strategy for reducing breast cancer mortality. Mammography has been used as the standard imaging method for breast cancer screening, with reduction in breast cancer mortality [7]. Breast density significantly reduces the ability to visualize cancers on mammography. The number of missed cancers is substantially increased in mammographically dense breasts, where the sensitivity is reported as low as 30 to 48%. [8]; and the odds of developing breast cancer 17.8 times higher [9]. Hand held ultrasound (HHUS) has been used to optimize the detection of cancers in mammographically dense breasts, but is limited due to technical factors, such as breast size, considerable user variability and reproducibility, technical skill, and time constraints, precluding HHUS as an effective screening modality for breast cancer [10][11] and [12]. Kelly described the use of 3D-automated breast ultrasound (ABUS) as an adjunct to mammography in the evaluation of non-palpable breast cancers in asymptomatic women. ABUS with mammography resulted in an increase in diagnostic yield from 3.6 per 1,000 with mammography alone, to 7.2 per 1,000 by adding ABUS, resulting in a mammography miss rate of 3.6 per 1,000 [13]. However, one of the limitations of the study was that it did not isolate dense breasts as an independent risk factor for developing breast cancer, where the detection rate should be expected to be higher. ABUS is FDA-approved in the United States for screening of women with dense breast parenchyma [14]. The purpose of this study was to demonstrate that ABUS increases the detection of non-palpable breast cancers in mammographically dense breasts when used as an adjunct diagnostic modality in asymptomatic women. This resulted in the subsequent detection of cancers missed by mammography of smaller size and stage, justifying the basis for the judicious use of implementing ABUS in conjunction with mammography in the dense breast screening population. The tabulated data was extrapolated based on known mammography screening utilization to show a cost-benefit of additional ABUS as a population based screening method.

2. Methods

2.1. Selection of participants

This study and the use of patient electronic health records were approved by an ethics committee appointed by the institute Board of Directors. The study design included two study groups, the control and test groups, in successive years. Each group was followed prospectively for 1 year. The control group consisted of women screened by digital mammography alone and stratified for breast density based on a Wolf classification of 50% or greater breast density (defined as the ‘mammographically dense breast’ for the purpose of this study). The second group consisted of women initially screening by digital mammography as having mammographically dense breasts, followed by automated breast ultrasound (ABUS). Each group was carefully selected on the basis of breast density and having no major pre-existing predictors of breast cancer, such personal or family history of breast cancer, or BRCA gene positive. In addition, the test group patients were not included in the screening group so as to eliminate impact on the results of the test group patients. The control group consisting of 4076 asymptomatic women designated as Wolf classification of 50% or greater breast density underwent stand-alone screening digital mammography between January 2009 and December 2009 using digital mammography (Selenia, Hologic Inc., Bedford, MA USA). The sensitivity, specificity, positive predictive value, and negative predictive value for biopsy recommendation were determined, in addition to data collection regarding the size and stage of cancers missed by mammography. The test group, consisting of 3418 asymptomatic women designated as Wolf classification of 50% or greater breast density, underwent stand-alone screening digital mammography between January 2010 and May 2011 using digital mammography (Selenia, Hologic Inc., Bedford, MA USA). This was followed by automated whole breast ultrasound (Somo-V. U-Systems, Sunnyvale, CA USA). The mammography-alone results were not used as control results in order to eliminate potential bias introduced by ABUS results on the mammography interpretations. In addition, mammography results were interpreted independently from ABUS results so as not to introduce bias. The sensitivity, specificity, positive predictive value, and negative predictive value for biopsy recommendation were determined, in addition to derived statistical data regarding the relative risk, and odds ratio for developing breast cancer.

2.2. Assessment of mammographic density

Mammographic density was assessed independently by radiologists on a dedicated mammography viewing workstation equipped with 5-Megapixel resolution. The radiologists were FDA-qualified in mammography, with at least 10 years experience in breast ultrasound, 24 months of which included ABUS. Two radiologists interpreted both the mammography and ABUS examinations under identical viewing conditions of 5-Megapixel resolution. The mammograms and ABUS studies were double read by two radiologists, with final consensus determination for each case. Mammograms were evaluated according to one of five categories of density (0%, 1 to 24%, 25 to 49%, 50 to 74%, and 75 to 100%) and only mammograms with breast density of 50% or greater were included in the control and test study groups.

2.3. 3D-Automated breast ultrasound evaluation

3D-Automated Breast Ultrasound (ABUS) is a computer-based system for evaluating the whole breast. The whole breast ultrasound system (Somo-V, U-Systems, Sunnyvale, CA USA) was used in combination with a 6 to 14 MHz broadband mechanical transducer attached to a rigid compression plate and arm, producing over 300 images per image acquisition obtained as coronal sweeps from the skin to the chest wall. The mechanical arm controls transducer speed and position, while a trained ultrasound technologist maintains appropriate contact pressure and vertical orientation to the skin. Interpretation and reporting time for an experienced radiologist is approximately 10 minutes per examination. The radiologist has cine functionality to simultaneously view breast images in the coronal, sagittal, and axial imaging planes.

2.4. Data collection

ABUS scan data was collected for location and size of breast masses and recorded in a radial or clock orientation consistent with American College of Radiology reporting lexicon. Studies were reported according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) six-point scale (0=incomplete, needs additional assessment; 1=normal; 2=benign; 3=probably benign; 4=suspicious; 5=highly suggestive of malignancy) [15] and [16]. For BI-RADS scores of 1, 2, and 3 on ABUS, patients were followed prospectively for 1 year to exclude cancers missed on both mammography and ABUS. For BI-RADS scores of 4 and 5, stereotactic hand held ultrasound (HHUS) biopsy was performed using 14 gauge or larger percutaneous biopsy. HHUS was employed because ABUS is presently not equipped with biopsy capability. If a benign non-high risk lesion was diagnosed, such as simple breast cysts, no further tissue sampling was performed. All non-cystic lesions were biopsied. Cystic lesions were identified as anechoic, thin walled lesions with posterior acoustic enhancement. All pathology proven breast malignancies were further staged using contrast volumetric/whole breast MR imaging (1.5T HDe Version 15.0/M4 with VIBRANT software, GE Medical Systems, Waukesha, WI USA.) with computer assisted detection (CADStream software, Merge Healthcare, Belleview WA USA). A final pathological stage was assigned by the pathologists in the usual manner in accordance with the American Joint Committee on Cancer (AJCC) TNM system guidelines. The pathologists were blinded to patient participation in the study and the method of cancer detection.

2.5. Statistical analysis

Calculations were made of the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), relative risk, odds risk, and attributable risk of breast cancer using MedCal version 12.2.1 software. Exact 95% confidence intervals (CI) were calculated for diagnostic yield. Statistical methods involved the Chi-square test statistic, which was used to compare the number of cancers detected by ABUS, based on the size of cancer. P-values of less than .05 were considered to indicate statistical significance. Attributable risk (AR) was calculated according to the following formula: AR=(RR − 1)Pc ÷ RR, where RR denotes relative risk of greater than 50%, and Pc prevalence of density of greater than 50% in case patients[17][18] and [19].

3. Results

Comparable interobserver diagnostic reliability (Kappa value of 0.98) was observed with mammography and ABUS examinations. In the control group (N=4076), the median age of participants with breast cancer (N=19) at the time of biopsy was 54 years, distributed as follows: 26% (5 out of 19) cancers occurred in women younger than age 50; 63% (12 out of 19) in women 50 to 69 years; and 11% (2 out of 19) over the age of 70 years. All cancers (N=19) were biopsy proven invasive ductal carcinoma. The sensitivity and specificity of stand-alone digital mammography were 76.00% (95% CI: 54.87% – 90.58%) and 98.2% (95% CI: 97.76% – 98.59%). The positive predictive value was 20.43% (95% CI: 12.78% – 30.05%) with a breast cancer prevalence rate of 0.60% (95% CI: 12.78% – 30.05%). The cancer detection rate was 4.6 per 1,000, with mean tumor size detected by mammography (N=19) of 21.3 mm. The average size of missed breast cancer (N=6) was 22.3 mm. The node positivity rate was 5% (1 of 19 cases). In the ABUS study group (N=3418), the median age of participants with breast cancer (N=42) at the time of biopsy was 57 years, distributed as follows: 17% (7 out of 42) cancers occurred in women younger than age 50; 64% (27 out of 42) in women 50 to 69 years; and 19% (8 out of 42) over the age of 70 years. The sensitivity and specificity of ABUS were 97.67% (95% CI: 87.67% – 99.61%) and 99.70%, (95% CI=99.46% – 99.86%), respectively, in mammographically dense breasts. The positive predictive value of ABUS was 80.77% (95% CI=67.46% – 90.36%), with a breast cancer prevalence rate of 1.25% (95% CI: 0.91% – 1.69%). The odds ratio of breast cancer in mammographically dense breasts determined by ABUS was 2.65 (95% CI: 1.54 – 4.57; P=0.0004). The cancer detection rate was 12.3 per 1,000. A 2.6-fold increase in cancer detection rate was observed between ABUS added to digital screening mammography compared to stand-alone digital screening mammography. Invasive breast cancer accounted for 81% (42 out of 52) solid breast masses detected by ABUS, of which 93% (39 out of 42) were invasive ductal carcinomas, and 7% (3 out of 42) were invasive lobular carcinomas. The mean tumor size detected by ABUS in patients with breast cancer (N=42) was 14.3 mm, distributed as follows: Stage 1A disease accounted for 83% (35 out of 42) of cases; 12% were Stage 2A (5 out of 42), and 5% were Stage 3A (2 out of 42). Stage 3A disease was associated with multifocal disease in both cases, one of which also was Level 1 axillary lymph node positive. The node positivity rate was 2% (1 in 42) of cases. The false positive rate of ABUS was 19.3%, with a negative predictive value of 99.97% (95% CI 99.83% – 100.00%). The pathologies associated with false positive results (N=10) were fibroadenomas and atypical epithelial neoplasms. We also used our data to extrapolate the theoretical cost-benefit of ABUS screening applied to a large screening population in the United States. Our analysis relied on the following assumptions: (1) Global Centers for Medicare and Medicaid reimbursement rate of breast ultrasound of $71 [20]; and (2) Estimated mean doubling time of a missed cancer of 250 days at the 95th percentile [21] and [22]. According to previously cited cancer kinetics models, a missed breast cancer should be clinically evident within 9 months[23]. When we considered the mean breast cancer size in our positive test subject group, 14.3 mm (N=42), we extrapolated a theoretical missed cancer size of 29.2 mm at 9 months in mammographically dense breasts, representative of Stage 2 or greater disease. In control subjects, a mean breast cancer size of 22.3 mm was consistent with stage 2 breast cancer. Incremental treatment cost assumptions, based on the global Centers for Medicare and Medicaid reimbursement rate between Stage 1 and Stage 2 breast cancer, were $24,002 and $34,469, respectively, for a cost differential of $10,467 [24]. Accordingly, the aggregate costs of screening 3418 ABUS patients in this study were $239,260, compared to the estimated aggregate costs of additional treatment in 26 potentially missed cancers (based on previously noted theoretical assumptions) of $275,557 based on a cancer miss rate of 0.77% (or 7.7 per 1,000).

4. Discussion

Table 1 shows the clinical indications for ordering an ABUS examination. Table 2 shows the distribution of breast cancer size according to age in the control and test study groups. The test group showed no statistical difference between size of the cancer and patient age at presentation. A significant increase in tumor size in the over 70 patients in control subjects was attributed to the more advanced tumor stage at presentation.Table 3 shows that stand-alone digital mammography was less sensitive than ABUS in breast cancer detection, with a 4-fold increase in positive predictive value of ABUS compared to stand-alone mammography in dense breasts. Our results showed that mammographic density of 50% or more was associated with an increased risk of breast cancer and resulted in a significant miss rate in asymptomatic women. Table 4 shows a statistically significant age-related attributable risk of developing breast cancer for mammographic density of 50% or greater. These observations are consistent with other studies which have shown an increased risk of breast cancer in dense breasts following negative mammography screening [2],[3][8] and [9]. We observed that breast cancer risk was highest in patients over age 70, where increased breast density was associated with an attributable risk of 29.6 (95% CI, 21.5 – 40.8). Fig. 1 shows box plots comparing case patients and control subjects according to age, with tumor sizes shown as a function of the odds ratio, relative risk, and attributable risk for each age category.

Table 1. Clinical criteria for ABUS screening
• As a supplement to mammography, screening for occult cancers in certain populations of women (such as those with dense fibroglandular breasts and/or with elevated risk of breast cancer);
• Imaging evaluation of non-palpable masses in women under 30 years of age who are not at high risk for development of breast cancer, and in lactating and pregnant women; and
• BI-RADS (American College of Radiology Breast Imaging Reporting and Data System) scoring classification class III, heterogeneously dense, with 50% to 74% or 75% to 100% breast density on mammography, without palpable mass.
Table 2. Breast cancer size according to method detection

T2

Table 3. Detection of breast cancer according to method
t3

Table 4. Risk of breast cancer according to method detection

t4
  1-s2.0-S0899707112002872-gr1

Fig. 1. Breast Cancer Staging and Risk Assessment by Screening Method Detection. Box plots comparing case patients and control subjects according to age (boxes A through D). Tumor sizes are shown as a function of the odds ratio, relative risk, and attributable risk for each age category. Bars represent the highest and lowest observed values with respect to individual variables (individually labeled with arrows).

Our study also showed that 3D-Automated Breast Ultrasound (ABUS) was an effective screening modality in mammographically dense breasts. Our extrapolated data suggest a breast cancer miss rate of 7.7 per 1,000 in mammographically dense breasts in asymptomatic women, which is higher compared to the cancer miss rate of 3.6 per 1,000 reported by Kelly using ABUS [13]. We attribute the increased breast cancer miss rate due to breast density, which was isolated as the principal risk factor in our study. Other studies have shown that the attributable risk of breast cancer for a mammographic density of 50% or greater was 40% for all cancers detected less than 12 months after a negative screening mammogram, and as high as 50% in women less than the age of 50. This marked increase in the risk of breast cancer associated with mammographic density of 50% or greater up to 12 months following screening directly reflects cancers that were present at the time of screening but went undetected due to masking by dense breast parenchyma [25],[26][27][28] and [29]. In the final analysis, there is the issue of the theoretical cost-benefit of adding ABUS screening to mammography in an otherwise healthy population. The importance of screening mammographically dense breasts with ABUS has particular relevance based on the small size and early stage of breast cancers. Our study showed a mean tumor size of 14.3 mm, representing stage 1 disease, which was present in 81% of patients. From our data, we derived theoretical population-based costs as a basis for the cost-benefit of ABUS in the United States population. Our study compared the incremental costs of screening versus the costs of added treatment related to a change in the staging of missed cancers from Stage 1 to Stage 2. The costs of additional treatment outweighed the costs of screening by $32,808, which calculated to $9.60 added healthcare cost per patient in the 3418 participants in the study. In the United States, 48 million mammograms were performed annually, with a reported estimated miss rate of 10% [30]. When comparing control versus test patients, our study suggests a theoretical miss rate of 7.7 cancers per 1,000 mammograms, or 0.77%, which is considerably lower than the reported missed rate of 10%. Based on these theoretical assumptions, annual added ABUS screening of the entire U.S. population would cost $3.40-billion. However, in actual practice, ABUS would be used only in the mammographically dense breast, which would potentially reduce the screening costs by at least a factor of 0.8, bringing the cost closer to $2.72-billion. By contrast, the incremental costs of added treatment associated with stage 2 compared to stage 1 breast cancer in the U.S. population would be $3.82-billion, assuming a conservative cost basis of $10,467 per patient.. The cost-benefit of early detection of stage 1 disease results in a theoretical per capital annual cost savings of $22.75 per screened patient in the U.S. population, according to our model. However, we have no actual or derived data to support improved breast cancer mortality with the addition of ABUS as a universal screening modality. This is one of the major limitations of our study because actuarial analyses used to justify screening modalities are typically based on mortality statistics. With respect to five year survival statistics between stage 1 and stage 2 breast cancers, of 98% and 80%, respectively, one could construe the potential for a theoretical quality-of-life benefit based on judicious ABUS screening. Another limitation of our study is the relatively small screening population used in our study, emphasizing the need for continued research in order to validate ABUS as a viable and cost-effective population-based screening modality, which should be stratified for other risk factors for breast cancer, such as: personal or family history of breast cancer, BRCA genetic results, environmental factors (late parity, previous exposure to ionizing radiation, exogenous estrogen, smoking, and alcohol use), early menarche/late menopause, and ethnic/racial differences. At most imaging centers, mammography is the only screening method for breast cancer detection. Our study corroborates with the data derived from other studies that the principal mechanism for breast cancer in dense breast parenchyma is not rapid growth, but rather, the masking of coincident cancers that are missed on screening mammograms [9]. These findings further suggest that the addition of mammographic screening in patients with dense breast parenchyma is likely not to increase diagnostic yield in the detection of breast cancers. Therefore, emphasis should be placed on alternative imaging techniques for such women. To conclude, our study of a small representative dense breast screening population showed that the addition of ABUS was more effective than digital mammography alone. This study provides a platform for using ABUS as cost-effective approach to breast cancer detection in the judicious screening of asymptomatic women with excessive mammographic density, in whom the greatest risk is between screening mammography examinations.

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Corresponding author. Breast Cancer Research Institute, Nova Southeastern University College of Medicine, 5732 Canton Cove, Winter Springs, FL 32708, USA. Tel.: 1 407 699 7787.

Copyright © 2013 Elsevier Inc. All rights reserved.

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