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Posts Tagged ‘FDA 510K’


Larry H Bernstein, MD, FCAP, Reporter    Reblog

http://pharmaceuticalintelligence.org/2013-11-17/larryhbern/cancer-biomarkers

Clinical Laboratory News Nov 2013;  39( 11)

  The Vicious Cycle of  Under-Valued Cancer Biomarkers

 Could Sweeping Changes Bring More Tests Into Clinical Practice?

By Genna Rollins

It’s a classic conundrum: biomarkers are essential to

  • diagnosing,
  • staging,
  • treating, and
  • monitoring cancer,

yet despite an explosion of research, only a trickle have made it into clinical practice. The factors behind this less-than-desirable circumstance are complex at best, but according to some observers, boil down to the healthcare system’s placing

  • more value on cancer therapeutics relative to biomarkers.

Without a better means of demonstrating the difference biomarkers make in clinical outcomes or management, they remain stuck in a loop of low value:

  1. inadequate funding for research that, in turn,
  2. limits the evidence for clinical utility,
  3. keeping their value low.

A panel of leading scholars, clinicians, and executives recently collaborated about this dilemma in the hopes of starting a national dialogue toward breaking what they call a vicious cycle. Their proposed solutions—as ambitious as the problem is convoluted—can be implemented if the industry has the will to do so, they contend.

“People don’t value tumor biomarkers. They value therapeutics. We all talk about personalized medicine but we don’t really mean it if we’re not willing to value biomarkers the way we value therapeutics,” said Daniel Hayes, MD, Stuart B. Padnos professor of breast cancer research and clinical director of the breast oncology program at the University of Michigan Comprehensive Cancer Center in Ann Arbor. “However,

  • if we insist on doing biomarker research the same way we do therapeutic research, it gets very expensive.

But without putting the kind of money and research into it that’s necessary to determine clinical utility, payers don’t want to pay as much for a diagnostic as a therapeutic, and we’re stuck in this vicious cycle.”

Hayes has been on the forefront of thinking about how to bring more clinically meaningful biomarkers into cancer care, and he was the lead author of the group’s commentary, which was published

  • in the July 31, 2013 issue of Science Translational Medicine (Sci Transl Med 2013;196:1–7).

 A Bad Track Record

Whether or not they concur with all elements of Hayes’ and his colleagues’ vicious cycle concept, many in the cancer field agree that biomarkers have an anemic batting average. The scientific literature is replete with reports of promising cancer biomarkers, but

  • fewer than two dozen protein tumor markers have been cleared by the Food and Drug Administration (FDA)—
  • only about half in the past decade—and not all have been embraced by clinicians.
  • The same is true of lab-developed tests (LDTs).

While some paradigm-shifting cancer biomarkers with obvious clinical utility have been implemented rapidly in practice, others—be they FDA-cleared or LDTs—never really have found a niche. An example of the latter is

  • UGT1A1 testing prior to starting irinotecan hydrochloride chemotherapy in colorectal cancer patients.

Individuals who are homozygous for the UGT1A1*28 allele are at increased risk for neutropenia when taking irinotecan, but this testing just hasn’t caught on, according to Hayes’ co-author, Richard L. Schilsky, MD, chief medical officer of the American Society of Clinical Oncology (ASCO). “Practically no oncologist orders the test because

  • it’s not clear what you’re supposed to do if you get a result back that shows your patient is high-risk.

There’s not a specific recommendation about whether you’re supposed to

  1. omit the drug,
  2. lower the dose,
  3. lower the dose by how much, and
  4. whether lowering the dose actually mitigates the side-effects and
  5. whether or not it actually reduces the effectiveness of the treatment,” he explained.

On the other end of the spectrum is KRAS genetic testing in metastatic colon cancer. After seminal research published in 2008 showed that

  • patients without this mutation were more likely to respond to anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibody therapy,

the test, even as an LDT, quickly became the de facto standard-of-care.

“A wealth of data came out all at the same time, and almost overnight

  • oncologists started ordering KRAS testing and
  • stopped prescribing the relevant drugs for patients whose tumors had KRAS mutations,” recalled Schilsky.

“That was a clear example of where if the tumor has a mutation,

  1. the drug doesn’t work, and
  2. you shouldn’t give it.

That’s the kind of discrete information that oncologists are always looking for.”

The KRAS test also hit the medical economics jackpot:

  • one anti-EGFR agent, cetuximab, costs anywhere from $110,000–160,000 per year,
  • making an easy argument for reserving this treatment for patients most likely to benefit from it.

At the same time, the cost of KRAS testing, about $400, speaks to Hayes’ and his colleagues’ arguments about the vicious cycle. If the test has that much clinical impact, shouldn’t it be valued higher in the medical system?

  Is Drug Development a Model?

“The pharmaceutical industry

  • potentially could be a model for how to do biomarker validation and evidence generation.

But then we’d have to charge a whole lot more for the tests, which everybody sees as commodities right now,” said Hayes’ co-author Debra Leonard, MD, PhD, professor and chair of pathology at the University of Vermont College of Medicine in Burlington.

  1. “Payers pay the cost of doing the test, but they
  2. aren’t calculating in the cost of all that evidence generation.

That’s why drug companies can charge so much, because all the cost of generating evidence is built into the price of a drug when it comes onto the market. There’s also good evidence to say that it does or doesn’t work, and

  • that’s not the case for tests.”

To Leonard’s point about evidence, the authors cited one cause of the vicious cycle as how FDA regulates approvals for diagnostic tests (See Figure, below). The agency by statute does not have authority to require that

  • proposed tests show clinical utility by improving clinical outcomes,

but that is exactly what the authors would like to see. “In the current regulatory environment, many tumor-biomarker tests

  • enter the market with analytical and clinical validity but insufficient information to establish their impact on healthcare outcomes.
  • Thus, few of these tests are included in evidence-based guidelines,

leaving healthcare professionals or third-party payers unsure of whether and how to use the tests or how much to pay for them,” they wrote.

 Vicious to Virtuous

A. Vicious Cycle

B. Virtuous Cycle

(A) The vicious cycle of tumor-biomarker research and clinical utility.

(B) A proposed virtuous cycle of tumor-biomarker research and clinical utility based on proposals herein.

Used with permission of Science Translational Medicine

  Shaking Up the FDA

The authors proposed several solutions to this challenge, some more audacious than others. On the bold side, they suggested that

  • FDA reorganize how it reviews oncology products, consolidating now separate drug and diagnostic reviews into a single oncologic product line managed jointly by the respective drug and biomarker divisions;
    • in the case of the former, the Office of Hematology and Oncology Products in the Center for Drug Evaluation and Research, and
    • in the latter, the Office of In Vitro Diagnostics in the Center for Devices and Radiological Health
  • In the same vein,
    • the authors proposed that FDA approve or clear tests only with rigorous evidence of both clinical utility and analytical validity, using ASCO level 1 evidence criteria.

If those recommendations might be long-term goals, possibly even requiring Congressional approval, others seem more approachable, but perhaps no less controversial. One is that

  • FDA begins regulating LDTs.

This contentious topic, under review for more than 3 years at FDA, would, the authors suggest, subject all proposed tests to a risk-based review process, regardless of the manufacturer or commercialization strategy behind them. However, not even all the authors back the idea.

“I don’t believe we can do away with LDTs,” said Leonard. “If the FDA had a better mechanism for looking at LDTs in their risk-based system, that might be helpful. But I worry about everything having to go through FDA because of the slowness of the FDA process and the expense of using the FDA process. There also doesn’t seem to be any idea of how we’re going to get from where we are today to an FDA approval process that actually works.”

Leonard also expressed skepticism that the FDA approval process inherently produces better or more clinically useful tests than do LDTs. “The FDA process does not look at

  1. clinical utility, and there is no evidence that
  2. FDA-cleared or -approved tests do any better when
    • they get into clinical practice than ones that haven’t gone through the FDA process and are LDTs,” she said,

citing the Health and Human Services Secretary’s Advisory Committee on Genetics, Health, and Society, which, in its 2008 report on the U.S. system of overseeing genetic testing found

  • a “paucity” of information about the clinical utility of genetic testing.

Other researchers who have thought about how to speed up the biomarker pipeline also find this recommendation troubling. “Basically eliminating LDTs, especially if this were not accompanied by a prior increase in reimbursement and research dollars, would be extremely negative,” said Leigh Anderson, PhD, CEO of Washington, D.C.-based SISCAPA Assay Technologies. “I’m involved in collaborative work with a number of groups trying to develop new tests mainly in the cancer area and I don’t think any of them would be in the position

  • to think seriously about going forward with those if the LDT route didn’t exist.

They would have to raise hundreds of millions of additional dollars to take that approach. It’s not trivial to develop an LDT, but to say

  • a proposed assay has to be treated as an FDA-cleared in vitro diagnostic [IVD] represents a significant additional barrier.”

  The Benefits of Biospecimen Banks

Although Anderson wasn’t on board with the authors’ proposal about LDTs, he lauded their recommendation that

  • all drug registration trials maintain a biospecimen bank,
  • funded by the sponsoring drug company,
  • so that subsequent researchers could access the samples for prospective-retrospective studies.

In fact, writing in a Clinical Chemistry opinion piece along with the journal’s editor-in-chief Nader Rifai, PhD, he recommended that

  • the National Institutes of Health develop a list of key clinical diagnostic questions
  • prioritized by disease impact and linked to studies or medical centers with corresponding biospecimens.

This “would allow

  • a much more informed and productive application of the existing biomarker resources and
  • would provide a much-needed basis for arguing for the enormous potential health-economic value of successful new tests,” they wrote (Clin Chem 2013;59:194–7).

Along with the need to provide higher levels of evidence for candidate cancer biomarkers, the authors called for a significant

  • ramp-up in biomarker research investment and higher reimbursement for tests that demonstrate clinical utility.

In addition, they recommended that

  • scientific journals adhere to higher standards in publishing tumor biomarker studies and
  • be as willing to publish biomarker studies that have negative results as those with positive findings.

Finally, they proposed that guideline bodies follow evidence-based recommendations for tumor biomarker test use.

  Emulating the PET Registry

To up the ante on these sweeping reforms, the authors believe addressing them in concert is the only way to break the vicious cycle. But how can the healthcare industry essentially reinvent a new paradigm for better valuing cancer biomarkers when the elements of doing so are like a gyrating Medusa’s head of knotty, seemingly intractable challenges? The authors agree the problem is too daunting if considered only in its entirety. But they and other experts suggest that several tangible actions could move the field along substantially without too much chaos or pain.

For example, in the area of building evidence that would open the door for better reimbursement for cancer biomarker tests,

  • Schilsky envisions a tissue or blood test equivalent to the National Oncologic PET Registry (NOPR).

This ground-breaking initiative managed by the American College of Radiology (ACR) and the ACR Imaging Network developed evidence for Medicare to reimburse PET scanning with F-18 fluorodeoxyglucose when it wasn’t covered at all. NOPR enabled reimbursement for this testing in cancer patients on the proviso that physicians agreed to enter data in a registry that would enable a fair assessment of the impact PET had on cancer patient management. Started in 2006, NOPR led in 2009 to coverage of PET scans as part of the initial treatment strategy for most solid tumors, coverage that recently

  • was expanded to include payment for up to three PET scans in patients with advanced cancer after their initial treatment, according to Schilsky.

By doing the same thing with selected tumor biomarkers, Schilsky suggested, “we immediately begin to capture information that we’re not currently getting on

  • the prevalence of use of certain tests,
  • the kinds of clinical decisions based on those tests, and
  • the outcomes of the patients who undergo the testing,”

he explained. “Then, for payers, it becomes much less of a Wild West environment. They will have information they can analyze and use to inform their coverage decisions.” Such a system also would differentiate the most clinically useful tests from less relevant ones, enabling payers to shift resources to the winning tests. This, in turn, would incentivize test developers “to put tests out that are likely to perform well,” Schilsky added.

The authors also point to efforts like the National Human Genome Research Institute’s recent decision to fund more than $25 million over 4 years to develop the Clinical Genome Resource for

  • authoritative information on genetic variants relevant to human disease and patient care.

The National Cancer Institute (NCI) also is planning a

  • web-based inventory of all biospecimens collected under its clinical trials cooperative group program, according to Schilsky.

  The Impact of Technology

Anderson believes the authors overlooked the impact technological advances could have on the vicious cycle, by speeding up the process of vetting candidate biomarkers. “An alternative way of doing mass spectrometry [MS]-based protein assays is

  • to analyze for specific targeted proteins in smaller numbers, so you might measure 10 or 100, but accurately and quickly,” he explained.

“Those kinds of directed assay methods which are not looking for everything, but instead for specific things that you hypothesize are important biomarkers, can be run

  • fast enough and cheaply enough that you can run hundreds and hundreds of samples in a practical way.

That then removes the primary technological limitation to getting the validation of biomarkers done.”

Anderson added that this type of MS-based directed protein analysis also could speed up the bench-to-bedside time for biomarkers. “The advantage is that the same method used in biomarker verification studies at the research stage can be

  • implemented at least in the large reference labs as LDTs, where
  • it provides a significant technology improvement over immunoassays,” said Anderson.

“That it can be taken all the way from research to LDT in a capable reference lab takes a lot of delay out of the introduction into clinical practice. That’s because you don’t need to redevelop immunoassays for different platforms. Eventually you might not even need to redevelop it for an IVD platform, once mass spectrometry IVD platforms exist.” He predicted that this approach could shave 5–10 years from the biomarker development process. Anderson’s company, SISCAPA Assay Technologies, provides mass-spec-based specific assays for biomarker proteins.

Researchers also have a responsibility to think about the clinical need they want to address before diving deep into discovery, suggested Ivan Blasutig, PhD, a clinical biochemist and assistant professor at Toronto General Hospital and the University of Toronto. “Many of the biomarkers discovered may have statistically significant results but when it comes to actual clinical use they don’t cut it. That’s one of the biggest issues,” he said.

 Analytics: The Achilles Heel

Blasutig collaborates closely with Eleftherios Diamandis, MD, PhD, who has written extensively about the challenges of bringing proposed cancer biomarkers into clinical practice. He, Diamandis, and others have emphasized how important robust analytics are in the early stages of biomarker discovery. In fact Blasutig and Diamandis recently wrote about how using what turned out to be an unreliable commercial kit for CUZD1 detection set back their research team by 2 years and about $500,000 in their quest to find a new pancreatic cancer biomarker (Clin Chem 2013 doi:10.1373/clinchem.2013.215236).

Blasutig and others encouraged clinical laboratorians to participate actively in biomarker discovery, as they bring a wealth of knowledge about analytical issues in validation that research chemists don’t necessarily share. Laboratorians also are more likely to be aware of resources like the NCI’s Early Detection Research Network, which gives guidance on topics such as completing sample collections and avoiding analytical bias, Blasutig suggested.

If the vicious cycle seems completely unwieldy and unrepairable, each person CLN contacted expressed confidence that even in the face of long odds, it can be changed. Leonard spoke for many: “I’m optimistic that we have to do this for our patients, and for our healthcare delivery system. There are a lot of good people around who are interested in having this conversation. So if we can just get all the parties at the table and see if there are some concrete steps that we can take, that would be a major step in the right direction.”

 Comment by reviewer:

The clinical laboratory has been concentrating on technical accuracy considerably beyond the clinical utility of many observations in clinical medicine.  This is not necessarily appreciated, so when a test is inconsistent with the clinical hypothesis, it may be rejected as error.  Errors may occur, but are rare, except if there is specimen misidentification.  However, we are still focused on a “silver bullet” approach to use of diagnostic tests.  There is some variability of the expression of cancer cells, so that there are subclusters to be expected within a major class.  The level of applied mathematics that is needed to analyze this data has been refined enormously in the last decade, and has to be used on the selected groups of tests referred to with all due respect by Leigh Anderson, who has the imagination to pursue the highest accuracy in large scale MS analysis that his laboratory has pursued for many years.  This reviewer is interested in the “information content” of a combination of tests, when the accuracy of testing is no longer an issue.  By combining the high throuput and lower cost of processing, with vastly better mathematical technology than is customary – on the fly – would be a breakthrough.  That would not be the end of this journey because there would have to be centers for analysis distributed within a few hours of the treatment centers (or at those sites), so that testing and processing would enable better facilitation of treatment.

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Many feedbacks to my last post reflected radiologists’ perception of ultrasound as a low-tech, unreliable imaging device.

Ultrasounds most manifested limitation by radiologists is that its performance is too-much user-dependent. This opinion finds support in numerous clinical studies concluding that ultrasound-based assessment of a cancer patient varies with the operator.

How come that an imaging technology that is not only  low-cost, simple to operate and risk-free to the patient, but has also gained a leading position in certain domain, like obstetrics,  is perceived as the underdog when it comes  to cancer assessment? Could it be because of its positioning as a “multi-purpose” system, which requires only very basic training?

If indeed this is the case, it doesn’t require “rocket-science” to turn it around. It only needs designing dedicated ultrasound machines who offer a comprehensive solution to one specific clinical need. Using such machines will require highly skilled operators who will enjoy a superior workflow, reporting tools and proven clinical guidelines.

The unsatisfactory reality of mammography-based breast cancer screening, as evident by epidemiology data and expert-panels’ reports, opens the opportunity to transform ultrasound into a winner in the niche-market of breast cancer screening and diagnosis. It’s a significant market that justifies the investment in ultrasound systems dedicated to detection and characterisation of breast cancer lesions.

No doubt, that the ability to provide accurate and standardized interpretation of such ultrasound systems’ scans is a pre-requisite. Ultrasound-based tissue characterisation is a must for any application aiming at standardized image interpretation. A sample out-of present ultrasound-based technologies aiming at providing some level of tissue-characterisation are listed below. Recent clinical studies show promising results using these technologies. It is worth watching carefully to see if any of those could be part of a future ultrasound-based solution to breast cancer screening.

Solid Breast Lesions: Clinical Experience with US-guided Diffuse Optical Tomography Combined with Conventional US

Results: Of the 136 biopsied lesions, 54 were carcinomas and 82 were benign. The average total hemoglobin concentration in the malignant group was 223.3 μmol/L ± 55.8 (standard deviation), and the average hemoglobin concentration in the benign group was 122.5 μmol/L ± 80.6 (P = .005). When the maximum hemoglobin concentration of 137.8 μmol/L was used as the threshold value, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of DOT with US localization were 96.3%, 65.9%, 65.0%, 96.4%, and 76.5%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of conventional US were 96.3%, 92.6%, 89.7%, 97.4%, and 93.4%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of conventional US combined with DOT were 100%, 93.9%, 91.5%, 100%, and 96.3%, respectively.

Conclusion: US-guided DOT combined with conventional US improves accuracy compared with DOT alone.

Breast Lesions: Quantitative Elastography with Supersonic Shear Imaging—Preliminary Results

 

 

Results: All breast lesions were detected at Supersonic Shear Imaging. Malignant lesions exhibited a mean elasticity value of 146.6 kPa ± 40.05 (standard deviation), whereas benign ones had an elasticity value of 45.3 kPa ± 41.1 (P < .001). Complicated cysts were differentiated from solid lesions because they had elasticity values of 0 kPa (no signal was retrieved from liquid areas).

Conclusion: Supersonic Shear Imaging provides quantitative elasticity measurements, thus adding complementary information that potentially could help in breast lesion characterization with B-mode US.

 Distinguishing Benign from Malignant Masses at Breast US: Combined US Elastography and Color Doppler US—Influence on Radiologist Accuracy

Results: The Az of B-mode US, US elastography, and Doppler US (average, 0.844; range, 0.797–0.876) was greater than that of B-mode US alone (average, 0.771; range, 0.738–0.798) for all readers (P = .001 for readers 1, 2, and 3; P < .001 for reader 4; P = .002 for reader 5). When both elastography and Doppler scores were negative, leading to strict downgrading, the specificity increased for all readers from an average of 25.3% (75.4 of 298; range, 6.4%–40.9%) to 34.0% (101.2 of 298; range, 26.5%–48.7%) (P < .001 for readers 1, 2, 4, and 5; P = .016 for reader 3) without a significant change in sensitivity.

Conclusion: Combined use of US elastography and color Doppler US increases both the accuracy in distinguishing benign from malignant masses and the specificity in decision-making for biopsy recommendation at B-mode US.

Evaluation of breast lesions by contrast enhanced ultrasound: Qualitative and quantitative analysis

A 57-year-old woman with a no-palpable lesion in the outer upper quadrant of left breast. (a) Gray scale image show an indistinct, hypo-echoic lesion. (b) Contrast enhanced image obtained 35 s after contrast agent injection showing a homogeneously and hyper-enhanced lesion. (c) Micro flow image obtained 38 s after contrast agent injection showing the enhanced mass with several radial vessels (arrow). (d) The time-intensity curve analysis show the peak intensity is 145.69 (intensity/1000), time to peak is 15.08 s, ascending slope is 8.98, descending slope is 1.03, the area under the curve is 7783.34. Pathologic analyses show this is an invasive ductal carcinoma.

Results: Histopathologic analysis of the 91 lesions revealed 44 benign and 47 malignant. For qualitative analysis, benign and malignant lesions differ significantly in enhancement patterns (p < 0.05). Malignant lesions more often showed heterogeneous and centripetal enhancement, whereas benign lesions mainly showed homogeneous and centrifugal enhancement. The detectable rate of peripheral radial or penetrating vessels was significantly higher in malignant lesions than in benign ones (p < 0.001). For quantitative analysis, malignant lesions showed significantly higher (p = 0.031) and faster enhancement (p = 0.025) than benign ones, and its time to peak was significantly shorter (p = 0.002). The areas under the ROC curve for qualitative, quantitative and combined analysis were 0.910 (Az1), 0.768 (Az2) and 0.926(Az3) respectively. The values of Az1 and Az3 were significantly higher than that for Az2 (p = 0.024 and p = 0.008, respectively). But there was no significant difference between the values of Az1 and Az3 (p = 0.625).

Conclusions: The diagnostic performance of qualitative and combined analysis was significantly higher than that for quantitative analysis. Although quantitative analysis has the potential to differentiate benign from malignant lesions, it has not yet improved the final diagnostic accuracy.

 Breast HistoScanning: the development of a novel technique to improve tissue characterization during breast ultrasound

Results: In 17 normal testing volumes, 3% of isolated voxels were classified as abnormal. In 15 abnormal testing volumes, the subclassifiers differentiated between malignant and benign tissue. BHS in benign tissue showed <1% abnormal voxels in cyst, hamartoma, papilloma and benign fibrosis. The fibroadenomas differed showing <5% and <24% abnormal voxels. Abnormal voxels in cancers increased with the volume of cancer at pathology.

Conclusions: HistoScanning reliably discriminated normal from abnormal tissue and could distinguish between benign and malignant lesions.

Written by: Dror Nir, PhD

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

Writer: Dror Nir, PhD

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.

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