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Featured Articles
Inconsistencies with breast density protocols can be solved
While everyone in the healthcare industry agrees that early detection of breast cancer saves lives, much less consensus can be found across the broader conversation of breast cancer screening in general. This inconsistency is especially apparent as it pertains to breast density, an issue that carries significant weight for both clinicians and patients. It is necessary for radiologists to not only acknowledge and understand how breast density impacts screening in general, but also to recognize the discrepancies in today’s breast density protocols, best practices for handling them and how this can affect clinicians and patients.
by Tracy Accardi
To start, consider the way a patient’s breast density is currently assessed. Most commonly, radiologists complete a visual assessment, which involves looking at digital images of the patient’s breasts and determining which of the categories her tissue fits into best according to a classification system known as the Breast Imaging Reporting and Data System (BI-RADS). There are four classifications to establish breast density type, which include – from least to most dense – fatty, scattered fibroglandular, heterogeneously dense, and extremely dense. Although the four categories help establish what radiologists should be looking for visually to determine if a woman has dense breasts, each radiologist’s individual perceptions are open to interpretation, potentially leading to inconsistencies in classification. As a result, some women may be misinformed about what their breast density is, which can be problematic considering breast density has long been recognized as a risk factor for cancer. In fact, women with very dense breasts are four to five times more likely to develop breast cancer than women with less dense breasts [1,2].
Screening protocol for dense breast patients
Once a woman’s breast density is classified, there is a good deal of debate regarding next steps for breast cancer screening. In fact, in a 2017 Kadence study, only 32 percent of the surveyed radiologists in Europe indicated they have a formal screening protocol in place for patients with dense breastS [3]. There are a number of modalities radiologists can choose to utilize when screening women for breast cancer, however, very dense breasts are challenging to read, particularly when using traditional 2D mammography. This is because suspicious calcifications appear white on a mammogram, blending in with dense breast tissue that is similar in colouring that is also known as a “masking effect.” Therefore, the imaging modality used to screen patients, especially those with dense breasts, truly matters. In the U.S., for example, Hologic’s 3D Mammography Exam is the only mammogram that is FDA-approved as superior to standard 2D mammography for routine breast cancer screening of all women, including those with dense breasts [4]. Despite this, there are no official guidelines that radiologists are encouraged to follow when screening their patients with dense breasts. As a result, patients may be missing the opportunity to receive a breast cancer diagnosis earlier on so they can start treatment right away because they weren’t screened with the most appropriate technology.
Clearly, there are many ways that clinicians across the world are currently approaching breast density protocols, especially as they pertain to assessment and screening. These inconsistencies are creating confusion among clinicians and patients alike. Fortunately, there are a number of solutions for this issue. When assessing density, radiologists should consider technology available to them to help remove subjectivity from their evaluations. In fact, clinicians can combine their patient-specific knowledge with artificial intelligence (AI), which—thanks to machine learning-based algorithms—can be used to classify breast tissue within the BI-RADS category, allowing for objective, accurate assessments. As a result, women can and should be better informed about what their breast density truly is, which may help those who didn’t realize they were at risk for cancer to be more compliant with screenings. Additionally, radiologists and their facilities should offer their patients the best possible technology that exists for screening dense breasts, pending they have no extenuating limitations based on their individual patient profiles.
Healthcare professionals owe it to their patients to find solutions that provide the best possible outcomes. By making breast density and the inconsistencies surrounding it a priority for reconciliation, radiologists can best deliver care to their patients.
References
1. Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 356(3):227-36, 2007.
2. Yaghjyan L, Colditz GA, Collins LC, et al. Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to tumor characteristics. J Natl Cancer Inst. 103(15):1179-89, 2011.
3. Kadence study conducted in partnership with Hologic in 2017. Data on file.
4. FDA submissions P080003, P080003/S001, P080003/S004, P080003/S005.
The author
Tracy Accardi, Global Vice President of Research & Development for Breast & Skeletal Solutions at Hologic, Inc.
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How artificial intelligence will change the role of doctors
The term “artificial intelligence”, defined in the Oxford Dictionary as “the capacity of a machine to simulate or surpass intelligent human behaviour”, was first used in the 1950s, but the idea that the human brain, or at least parts of it, could somehow be ‘copied’ or ‘imitated’ using a series of mechanical networks has been part of myth and literature for centuries, as far back as, for example, the automatons of Hephaestus and Daedalus.
by Prof Jean-Louis Vincent
Artificial intelligence is an umbrella term that covers multiple tools and technologies. In recent years, huge advances in machine learning algorithms, neural networks, deep learning, facial recognition, computing power and statistical techniques have increased the potential applications of artificial intelligence across multiple aspects of everyday life. In terms of medicine, artificial intelligence is already beginning to find a place in disease prediction and diagnosis, clinical decision support and therapeutic guidance, prognostication, and remote follow-up and monitoring.
So, what do these new developments mean for the medical profession – is this ever advancing technology going to decrease the need for doctors? To some extent, the answer is of course yes, perhaps particularly in the field of diagnostics. Using machine learning, computerized systems are able to study and develop pattern recognition from millions of test results over a very short period of time, whereas a doctor can only see and process a limited number of events throughout a whole professional career. The vast amounts of data becoming available as increasing numbers of patients have electronic health records and are managed with electronic monitoring systems are providing the input needed for artificial intelligence to develop, while also reducing the administrative load on doctors and other healthcare staff, a common source of complaint. Machine-based interpretation of visual data is now more accurate, effective, and rapid than the human eye and associated with less risk of error. Interpretation of X-rays and other medical images will become increasingly automated, reducing the need for radiologists to perform this role. Similarly, electrocardiograms are already read automatically; many of the reports are still validated by a cardiologist, but is this really necessary? In ophthalmology, examination of the fundus for many diagnoses, including diabetic retinopathy and macular degeneration, can be performed more accurately by computerized algorithms than by most experienced ophthalmologists. Application of deep learning to a database of eye CT-scans at a London hospital has enabled more than 50 ocular diseases to be recognized by the system. Non-image-based diagnosis is also possible. A pilot system developed in California that applied machine learning to electronic health record data has been used to diagnose common pediatric problems and shown to perform better than junior doctors.
Although artificial intelligence may therefore indeed reduce the need for doctors in some areas, this is not necessarily a bad thing; it should not be seen as the enemy, but rather as a means of improving medical practice and patient care. More rapid, accurate diagnosis can only be positive. Another important example of how artificial intelligence can be beneficial is in predictive medicine. Natural language processing of data from electronic medical records was able to more accurately predict postoperative complications in patients undergoing major surgery than were patient safety indicators. Machine learning-based sepsis prediction has been associated with reduced mortality rates. Indeed, early identification of patients with an increased risk of sepsis raises awareness and facilitates early investigation and management by sepsis-trained physicians in a condition where rapid diagnosis and treatment are crucial to maximize chances of survival. More broadly, identification of patients at risk of deterioration on the general floor or after surgery will enable appropriate monitoring systems to be offered to a selection of patients in an environment where it is not feasible, financially or practically, to monitor every hospitalized patient.
Another example is the field of therapeutics. Machine-learning can identity the most effective therapeutic algorithms for individual patients, with the most appropriate evaluation and follow-up. These algorithms are being incorporated into closed-loop systems enabling appropriate treatments to be administered and doses adjusted in real-time for individual patients according to continual personal data provision. Just some of many examples include implantable cardioversion defibrillators, diabetes management with implantable glucose monitors and insulin pumps, and use of vasopressors to prevent hypotension during surgery.
Artificial intelligence can thus be used to provide continually adjusted, up-to-date, patient-relevant information on the best therapeutic options for a specific patient much more rapidly and accurately than doctors can. Doctors can currently assimilate only a small portion of the information available on which to base such decisions. By configuring the huge amounts of patient data now available into predictive models, machine learning can provide physicians with recommendations regarding the optimum treatment schedule for a specific patient with a specific condition(s). Doctors are still needed to interpret the recommendation, explain them to the patient and their family, and initiate the chosen treatment course. Using artificial intelligence, doctors will be more enabled to select the optimum treatment regimens for individual patients, rather than the one-treatment-fits-all packages that are still used for many conditions today. Moreover, doctors will have greater access to accurate statistics to inform discussions with patients and their families, both about the likely outcomes from different treatment options, but also about likely prognosis if treatment is refused or withdrawn. The role of doctors in this situation will increasingly be one of advisor as patients become ever more involved in their own healthcare decisions.
Artificial intelligence is going to continue to invade every aspect of daily life, including medicine, and needs to be carefully developed and validated. Although currently very much associated with rich economies of the developed world, artificial intelligence could play a large role in improving healthcare in countries where qualified doctors are less widely available. These new technologies could therefore help to decrease inequalities in the health sector. Even in low income countries, the combination of artificial intelligence with telemedicine can help increase access to high quality medicine. Wherever it is employed, it will not entirely replace doctors but rather make them more efficient, provide more time for one-to-one patient contact and help provide care best adapted to individual patients, with reduced errors. People often raise the question of associated costs. But costs may not be very high, especially as the technology becomes more widely used. Indeed, artificial intelligence may even help reduce global healthcare costs by increasing efficiency.
Doctors should not fear this technology, but learn to accept and embrace it, determining and directing how it can best be incorporated into medical practice to improve patient care.
The author
Jean-Louis Vincent, MD, PhD
Dept of Intensive Care, Erasme Hospital, Université libre de Bruxelles, Brussels, Belgium
Suggested reading:
Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA 2016;315:551-2.
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342-1350.
Joosten A, Alexander B, Duranteau J, et al. Feasibility of closed-loop titration of norepinephrine infusion in patients undergoing moderate- and high-risk surgery. Br J Anaesth 2019 Jun 26. doi: 10.1016/j.bja.2019.04.064129
Kilbride MK, Joffe S. The new age of patient autonomy implications for the patient-physician relationship. JAMA 2018;320:1973-1974
Liang H, Tsui BY, Ni H, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med 2019;25:433-438.
McCoy A, Das R. Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Qual 2017;6:e000158
Murff HJ, FitzHenry F, Matheny ME, et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011;306:848–855.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56
Vincent JL, Creteur J. Big data are here to stay! Anaesth Crit Care Pain Med 2019;38:339-340.
Vincent JL, Einav S, Pearse R, et al. Improving detection of patient deterioration in the general hospital ward environment. Eur J Anaesthesiol 2018;35:325–333.
Contrast Enhancement: Expanding Frontiers of Ultrasound
Contrast enhanced agents have been key to enhancing the diagnostic capability of computed tomography (CT), magnetic resonance imaging (MRI) and clinical radiography. Since the turn of the millennium, contrast enhancement for ultrasound (CEUS) has also emerged as an imaging tool. Along with developments in scanning hardware, new contrast agents have expanded the application envelope of ultrasound. During CEUS, tiny liquid suspensions of biodegradable gas-filled microspheres (also known as ‘microbubbles’) are injected as tracer for microscopic ultrasound imaging examinations. The microbubbles are metabolized and expelled from the body within minutes. Clinical applications for ultrasound contrast agents potentially extend to any organ or physiological system that is evaluated with conventional ultrasound, with the singular exception of the fetus. As of now, major applications are in cardiac and hepatic imaging. Other applications are being explored, including paediatric CEUS.
From imaging complement to alternative
There is growing evidence that CEUS is valuable, accurate and cost-effective. It often complements CT and MRI, and in several instances, has become an important alternative to either. This especially concerns patients with renal failure, those who wish to avoid the radiation risk of CT or cannot cope with being shut inside a scanner.
Interest in CEUS has grown sharply since 2016, after the Food and Drug Administration (FDA) approved a microbubble contrast agent for liver CEUS, paving the way for much faster growth in the US market.
The microvascular challenge
Clinically, one of the key drivers for CEUS has been limits to the performance of ultrasound imaging and Doppler techniques. While B-Mode provides anatomical information, Doppler allows for visualization of the larger vessels in the macrovascular system, based on the velocity of blood flow in the intravascular lumen. However, there are limits to both spatial resolution and Doppler sensitivity.
The utility of conventional ultrasound reduces rapidly when a clinician needs to visualise smaller vessels and capillaries, lying within deeper structures of the body’s microvascular system.
To achieve this, and more specifically, determine differences in arrival-, dwell- and wash-out time within specific regions of parenchymal tissue, there is a need for direct imaging via tracers. It is in this capacity that contrast agents play a useful role. They improve the sensitivity and specificity of ultrasound and greatly expand its scope for application.
The advantages of CEUS
CEUS has certain intrinsic advantages when compared to other imaging modalities.
It permits ultra-high temporal imaging of contrast enhancement profiles at between 20 and 50 images per second, for a duration of about 5-8 minutes. This makes it possible for continuous visualization of images in all phases – from the early arterial to the late phase – and seek to ensure no patterns are missed. CEUS also allows for both follow-up examinations at short intervals, and, given its lack of ionising radiation, for repeated examinations over a long period of time – a common requirement for chronic diseases. CEUS is also convenient. It can be used at multiple bedsite locations – from intensive care units (ICUs) and operating rooms to recovery rooms and ambulatory units.
Contrast agents for ultrasound have been found to be safe with no cardio-, hepato-, or nephro-toxic effects. Laboratory checks to assess liver, renal or thyroid function before administration are therefore not required.
Evaluating liver lesions
In the liver, CEUS has proven its utility when clinicians encounter focal lesions during cross-sectional imaging of an asymptomatic patient. Though most such collateral encounters are benign, it is necessary to pursue dedicated imaging characterization and diagnosis, in order to exclude malignancy. This is especially true when the lesions are large or otherwise atypical and when the patient is from a high-risk group.
Traditionally, the evaluation of lesions was undertaken with magnetic resonance imaging (MRI) or multiphase CT. However, the former was generally limited in availability, while multiphase CT invoked concerns about radiation. CEUS is seen to be safe, non-invasive and available.
When CEUS is used in the liver, microbubble delivery occurs via two routes, namely the hepatic artery and portal vein. Blood flow through the latter needs to first transit gastrointestinal circulation, and therefore arrives at a later time point. This permits differentiation between the two wash-in phases.
CEUS enhances the display of vascularity in liver lesions, and is both accurate and reproducible. The vascular supply for focal liver lesions is characteristic of a particular lesion type and different from normal liver tissue. While abnormal vascularity of hepatocellular carcinoma can be demonstrated early during the contrast inflow phase, metastases are characterised in the late phase. In addition, the timing and the intensity of washout can differentiate hepatocellular malignancies from non-hepatocellular ones. The former demonstrate delayed and weak washout. Non-hepatocellular tumours show strong, early washout.
The need for right dosing
Using the optimal dose is important. Too high a contrast agent dose results in artefacts, particularly in the early phases of enhancement. These include acoustic shadowing, over-enhancement of small structures and signal saturation, which is also detrimental for quantification.
On the other hand, a low dosage causes the concentration of microbubbles to be sub-diagnostic in the late phase, challenging the detection of wash out.
If the wash out is early, the dose was probably too low. Here, it can be important to evaluate the status of the liver as being healthy or diseased. In difficult cases, a second (higher) dose may be administered, with no or only limited scanning in the early phases to reduce bubble destruction. The exact dose depends on the contrast agent, ultrasound equipment (software version, transducer), type of examination, organ and target lesion, size and age of the patient.
Other challenges for CEUS in the liver
Apart from the challenge of dosing, there are other limitations too in the use of CEUS in the liver. Very small lesions may be overlooked. The smallest detectable lesions are considered to be 3-5 mm in diameter.
There are also some specific shortcomings, such as fat layers surrounding the falciform ligament. These can cause enhancement defects which might be confused with a lesion.
Given limits to penetration, deep-seated lesions may also not always be accessible. However, some clinicians suggest that bringing the liver closer to the transducer via use of left lateral decubitus positioning can overcome such a limitation.
CEUS and cardiology
CEUS has also shown remarkable utility in cardiology. After the tracer injection, micro-bubbles follow the flow and distribution of red blood cells. opacify the cardiac chambers and enhance delineation of the left ventricular border. The microbubbles are then ejected into the arterial circulatory system, allowing for visualization of blood flow into the parenchymal organs.
An assessment of cardiac function depends on proper delineation of the endocardial border and wall motion patterns. This is where conventional ultrasound faces serious limits. Intracardiac echo reflections couple to weak signals from structures in parallel to the echo beam. The ensuing delineation of the endocardial border can therefore be unclear, resulting in an inaccurate left ventricle assessment.
What contrast agents achieve here is to completely fill the ventricular cavity, and thereby delineate it in a similar fashion to cardiac MRI.
Proper assessment of cardiac function is especially important for stress echo tests in order to demonstrate inducible ischaemia. Here, the risk of a stress examination means that inadequate image quality is unacceptable. In addition, precise delineation of the cardiac chamber is required to make an assessment of heart insufficiency and decide on whether an automatic implantable cardioverter defibrillator (AICD) is indicated. Such precision is also required with cancer chemotherapy patients, in order to assess cardiotoxicity.
New contrast agents
First-generation ultrasound contrast agents were based on air, which was sufficiently soluble in blood for use with the equipment of the time. Second-generation agents contain an inert lipophilic gas with very low solubility, thus avoiding early leakage of the gas. This provides more stability to the microbubbles.
Modern contrast agents have a shell made out of a thin and flexible phospholipid membrane. One side, which faces the surrounding blood, has hydrophilic properties. On the other, lipophilic chains make contact with the encapsulated gas.
Over recent years, technology development has focused on ultrasound contrast agents which reduce microbubble size and increase persistence within the blood in the circulatory system, to 10 or more minutes. Researchers are also seeking to develop new materials and gases to control the encapsulating shell or surface of the microbubble, in order to inhibit dissolution and diffusion.
Constraints faced by microbubbles
In spite of the above developments, there are some constraints with microbubbles.
They do not last long in circulation, due to being taken up by immune system cells, the liver or spleen. They also have low adhesion efficiency, which means only a small fraction bind to an area of interest. Microbubbles can also burst at low ultrasound frequencies and at high mechanical indices, which, in turn, can lead to local microvasculature ruptures and haemolysis.
Guidelines on CEUS
The use of CEUS varies widely from one country to another, and even between different healthcare facilities in the same country.
Guidelines were first issued for the use of CEUS for liver applications in 2004. They were updated in 2008, reflecting growth in the availability of contrast agents. CEUS has also been recommended in guidelines for several non-liver applications, under the auspices of EFSUMB.
The latest guidelines date to 2012. They are published under the auspices of the World Federation for Ultrasound in Medicine and Biology (WFUMB) and the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB). The aim is to create standard protocols for CEUS in liver applications across the world.
According to the guidelines, CEUS is indicated for liver lesion characterization in the following clinical situations:
• Incidental findings on routine ultrasound
• Lesion(s) or suspected lesion(s) detected with US in patients with a known history of a malignancy, as an alternative to CT or MRI
• Need for a contrast study when CT and MRI contrast are contraindicated
• Inconclusive MRI/CT
• Inconclusive cytology/histology results
Paediatric applications
One new frontier for CEUS applications consist of children.
Currently, sulphur hexafluoride gas microbubbles have been approved by the FDA in the US for characterising focal liver lesions in children and vesico-ureteral reflux. In Europe, CEUS in children is indicated for vesico-ureteral reflux, although there is
significant off-label use too.
