In previous columns, I’ve referenced the adaptive value of prioritizing systems over goals. The concept is far from new to me; I think I’ve always instinctively lived that way. As with so many things, however, hearing it verbalized by someone else crystallized it for recognition by my conscious mind.

That someone is Scott Adams. He’s fleshed out the idea in a couple of his books (notably 

How to Fail at Almost Everything and Still Win Big

), but offers a pithy challenge/summary: Goals are for losers, systems are for winners.

This can seem like a slap in the face. An awful lot of conventional wisdom out there urges us to set goals, so we’ll have things to work towards. We’re pretty thoroughly indoctrinated with goal-setting before we finish our first decade of life.

A slightly longer-winded explanation he offers: “Goal-oriented people exist in a state of continuous pre-success failure at best, and permanent failure at worst if things never work out. Systems people succeed every time they apply their systems, in the sense that they did what they intended to do.”

For instance, let’s say you’re in the common position of wanting to lose weight. The scale says you currently weigh X, and you set a goal of weighing X minus 20. Maybe you give yourself a deadline: Gotta do it by swimsuit-season.

The problem: No matter what you do, until you cross the finishing-line and the scale confirms you are now X-20, you’re in that state of failure mentioned above, “pre-success” or otherwise. Doesn’t feel good. Can actually drain your motivation. And, if you set a deadline, each day that brings it closer intensifies those unhelpful sentiments.

Further, suppose you do manage to achieve your goal. Hurrah, you high-five yourself and scratch that item off your mental to-do list. No longer pursuing that goal, you might not maintain the behaviors that brought you to it. What happens? A few months later, you’ve regained the weight, which feels an awful lot like failure.

More adaptive is to focus not on the finishing-line of that goal, but a course you can chart that will take you in that direction. A system that, if adhered to, will achieve what you want or a good measure of it. You know that losing weight can result from burning more calories than you consume, getting sufficient sleep, etc.

So you incorporate such measures into your daily routine. That feeling of success, of “I can do this,” is no longer reserved for getting on the scale and seeing that magical number. Now, you can feel it whenever you do your daily exercise-routine, eat a non-overindulgent meal, get to bed on time, etc. Those little tastes of success feel good and help you stay on track…even after swimsuit season has come and gone.

And if you have a lapse? Miss some or even all of your regimen one day? That’s just one iteration of your system; it doesn’t leave you feeling like the whole thing has collapsed. It might even motivate you that much more strongly to get back on the horse the next day.

Another Adams quote: “A goal gives you one way to win, whereas a system can surface lots of winning paths, some of which you never could have imagined.” Suppose you get to swimsuit-season and your scale still shows a higher number than you’d like…but you look in the mirror, and realize that your system/regimen has redistributed your weight such that you look and feel better than you’d have thought possible. Not to mention the other benefits of getting more sleep, eating healthier, etc.

The lowest common denominator of most effective systems I can think of: Identify what works, and what detracts. Do more of the former, and avoid the latter. An iteration of a good system incrementally brings you closer to the things you want (even if you don’t initially recognize them), and keeps the things you don’t further at bay.

Good systems are, in effect, a personal (or team-based), super-accelerated form of evolution. “Survival of the fittest” behaviors to get you ahead…of where you were before, or relative to competitors.

So, how does this circle back to your radiology career? Really, how could it not?

Don’t, for instance, adopt the “goal” of zero misses on QA…because, if you do, every time somebody takes a shot at you, however petty, your failure-alarm will go off. Your only sense of success would be a pristine record each month/quarter/year. As bad as setting goals can be, making any of yours synonymous with perfection is downright toxic: All it takes is one bad moment to render you imperfect, and thus a failure.

Instead, develop systems of minimizing your “misses.” That includes honing your accuracy, but it can also mean learning turns of phrase that render your reports more difficult targets for nitpickers to play “gotcha.” And learning how to effectively defend yourself when disputing a case for the QA-committee.

Setting goals for productivity can also be a bad move. If you determine a need to read X number of cases, you’re living in failure until you hit that X. Worse, if you’ve divided your time by X to see how many minutes you have per case to remain on-target, you have that many more episodes of potential failure each hour.

Instead, develop a system that optimizes your performance. Find PACS hanging-protocols that best suit your style. Develop dictation-macros that speed your reporting. Keep your most-frequently used references bookmarked for easy access. And get disciplined with your work-cadence: Minimize avoidable interruptions, including self-inflicted ones like checking stocks, social media, etc.

And yes, this column’s title did mention giving yourself a raise in the context of all of this…I’ll be delving into that next week.

Articles

Focus not on the finishing line, but on the course you can chart that will take you in that direction.

Give Yourself a Raise: Goals and Systems in Your Radiology Career, Part 1

Axillary ultrasound evaluation may not be necessary in all patients with breast cancer, and a new article offers an algorithm to help radiologists determine which patients should undergo evaluation and which patients should skip it.

, represents a middle-ground approach between two opposing viewpoints -- those arguing for axillary ultrasound in all patients with newly diagnosed breast cancer and those arguing against it for all patients.

“Over the past decade the treatment algorithms for axillary nodal disease in patients with breast cancer have evolved,” Mansi Saksena, MD, of Massachusetts General Hospital told 

. “Hence, algorithms for imaging the axilla with ultrasound must also change. Many radiologists have not changed their practice based on recent changes in treatments (my second paper under publication shows data supporting the same). Our paper shows an imaging algorithm that will help radiologists decide which patients should get an axillary ultrasound and which should not.”

The report suggests axillary ultrasound for patients with palpable axillary nodes, T3/T4 tumors, multicentric or multifocal disease, those receiving neoadjuvant chemotherapy or endocrine therapy, and other scenarios as determined through multidisciplinary consultation.

Axillary nodal evaluation is unnecessary for patients with T1/T2 tumors who meet criteria for inclusion in the American College of Surgeons Oncology group Z0011 trial algorithm.

“The biggest challenge is in establishing uniformity in axillary nodal imaging,” Saksena said. “There is a lack of guidelines for axillary imaging, and this leads confusion about appropriate use of axillary ultrasound in patients with breast cancer.”

The report details indications for when axillary ultrasound is always, never or sometimes appropriate. Evaluation is sometimes indicated for patients with positive sentinel lymph node biopsy (SLNB), when breast MRI shows suspicious internal mammary nodes or axillary nodes in clinically node-negative patients, and patients older than 70 who will not undergo SLNB.

“I want practitioners to understand that they must use axillary ultrasound judiciously in patients with breast cancer,” Saksena said. “Doing axillary ultrasound in all patients can lead to unintended harm. Each practice must thoughtfully come up with imaging algorithms in collaboration with treating surgeons and oncologists.”

Potential harmful consequences of unnecessary axillary ultrasound include cost and resource burden on health systems and the potential for unnecessary preoperative biopsies or axillary lymph node dissection on patients.

“Although in many cases imaging plays a crucial role in the assessment of the axilla, it is essential that targeted axillary US and/or image-guided biopsy of an axillary lymph node be performed by the radiologist only when clinically appropriate,” Victoria Mango, MD; Melissa Pilewski, MD; and Maxine Jochelson, MD, wrote in a 

. “This decision-making process requires multidisciplinary communication and collaboration among radiologists, surgeons, medical oncologists and radiation oncologists.”

Decisions about axillary imaging should consider local clinical practices and adapt to changes in clinical management of axillary lymph nodes, the authors noted. Indications may change as more clinical trial results become available.

“The next step is to create educational tools and guidelines for axillary nodal evaluation with ultrasound in patients with breast cancer,” Saksena said.

Investigators at Massachusetts General Hospital proposed an algorithm for appropriate axillary nodal imaging to better align with changes in treatment algorithms for axillary nodal disease in patients with breast cancer.

Proposed Algorithm for Axillary Ultrasound Evaluation in Breast Cancer Patients Seeks Middle Ground

Racial disparities in health care have gained increased awareness and urgency, underscored by the inequitable effects of the COVID-19 pandemic along with a backlash to high-profile incidents of racial injustice.

, proposes a solution to the current lack of an organized and nationally accepted educational program for practicing radiologists to address diversity and healthcare disparity.

The article, written by Miral M. Patel, MD, and Jay R. Parikh, MD, FSBI, FACR, of the Division of Diagnostic Imaging at the University of Texas MD Anderson Cancer Center, calls for a three-pronged education approach to address health care disparities in radiology -- individual education, institutional change and national cooperation.

“Our intention is to further increase awareness of healthcare disparities in order to ultimately empower radiologists to work both with their respective institutions and national organizations to incrementally reduce these disparities,” Patel told 

The article noted disparities specific to radiology. For example, a 2011 trial found that low dose computed tomography was associated with a 20% reduction in mortality specific to lung cancer compared with chest radiography, however, Black Americans may be less likely to meet eligibility criteria. Similarly, Black patients may be hindered from prostate MRI and breast cancer screening.

“As our primary daily practice is that of breast imaging, a subspecialty in radiology, it was eye-opening to see how widespread healthcare disparities are among all radiology subspecialties, which again reinforces the role we as radiologists can play in creating change,” Patel said.

The article pointed out disparities in the use of imaging to evaluate acute ischemic stroke related to whether patients were uninsured, on Medicaid or Medicare, or had private insurance. Also, race was found to be an independent predictor of imaging missed care opportunities

Efforts have been made to diversify the workforce in health care and to educate medical students and trainees about disparities. Miral and Parikh called for an educational curriculum for practicing radiologists with a consolidated web-based training program that is updated annually.

The components of diversity education include 1) acknowledge that disparity exists, 2) understanding one’s implicit bias, 3) cultural competency training specific to radiology, and 4) raising awareness of radiology political action committees.

“We would like to increase clinician and health care provider awareness of these issues, but also to increase institutional awareness as any attempt at change requires multiple layers of support, including institutional motivation to partner with radiologists to find solutions,” Patel said.

Miral and Parikh called for institutional change similar to that initiated by a 2002 report by the Institute of Medicine titled “Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare.”

They said institutional cultural change requires mandates from leaders along with support of health care professionals to alleviate cognitive overloading.

National cooperation is necessary to advance beyond current discussions to action. The article proposed a co-sponsored national meeting of radiologist from a variety of subspecialties, regions and backgrounds to brainstorm potential solutions. National support also could raise awareness of limitations of insurance coverage that contribute to disparities, empowering radiologists to advocate for change.

“The next steps are leveraging the influence of national radiology organizations to work together in educating, brainstorming, and empowering radiologists and the respective institutions in which they practice to create practice-specific changes at the local level,” Patel said.

A recent article in Clinical Imaging suggests a three-pronged education approach to address healthcare disparities in radiology.

Proposal Calls for Educating, Empowering Radiologists to Address Healthcare Disparity 

A couple years ago, I borrowed from Glengarry Glen Ross when I wrote about how one should ABR (Always Be Recruiting). More important is ABL, spelled out in today’s title.

The principle would be one of health care’s Ten Commandments, if we had any. Should probably make the top three. There are rules/regs aplenty regarding CME to keep docs walking the walk. Even without those, medicine practically forces us to keep current as new developments constantly bombard us from all sides. It would be difficult not to learn.

So we pick up pearls of wisdom here and there. Some fill in gaps from our education and training. Others are more advanced: A weirdo lesion crosses our path, and we have to figure out what we’re looking at from reference-sources or charitable colleagues. Presto, we have our answer (or a reasonable differential), and now we’ve learned what to say if we ever see the lesion again.

There’s a lot of other potential learning that’s more easily missed. Stuff that doesn’t directly show up on diagnostic imaging or reports of same, and it has no CME. The biggest obstacle to this learning is failure to recognize opportunities for it. Sometimes, even when we think we’re learning something…it’s the wrong stuff.

The tricky part is that the best learning opportunities arise in situations of adversity, when you’re most likely to be distracted. Think about it: When everything’s hunky-dory, what’re you liable to learn? Keep on doing whatever you have been, since the status quo is exactly as you’d wish? Big whoop. No, it’s in circumstances most undesirable to you when you have the greatest room for improvement.

Take, for instance, the situation which inspired my blog from two weeks ago: Malicious hacking took down my rad group’s electronic infrastructure. Teleradiologists such as myself were suddenly unable to work offsite. No work equals no income, which resulted in a decidedly uncomfortable situation for everyone involved.

Visited with such misfortune, folks commonly learn the wrong lessons: “Life’s not fair,” “We make our plans and God laughs at them,” “You can’t count on anybody else,” “Bad luck happens,” “I probably don’t deserve any better,” etc.

Unfortunate truths? Maybe…but they don’t help you improve things, this time around or the next. And let’s face it, by the time you’re an adult, you’ve long since learned such “lessons” time and again. Repetitively self-flagellating with them doesn’t yield new wisdom. It might, however, prevent you from learning new, useful stuff.

Preoccupied by wallowing in self-pity or bitterness at the cold, cruel world, you’re not in the right frame of mind to adapt and overcome. It would be better to recognize what you might have done to defend yourself, what you can do now in the name of damage control, and even hidden opportunities that this turn of events might be presenting you so you can turn your situational lemons into lemonade.

This isn’t always about personal catastrophe or life-altering epiphany. Even the smallest annoyances can provide useful lessons if you’re paying attention. Consider, for instance, a “ding” to your QA. Irksome to the best of us. Sure, some of the time you’re genuinely learning from a mistake you made…but then there are those times where all you’re learning is “I hate QA,” “Dr. So-and-so is a nitpicker,” “This rad group’s process for adjudicating QA stinks,” etc.

Again, hardly adaptive/useful learning. But what if you take the opportunity to scrutinize the petty ways that QA system gets at you? Learn different turns of phrase to use in your reports so Dr. So-n-so has fewer opportunities to play “gotcha.” Take note of how the QA system fails to live up to its purpose. Maybe develop yourself as an expert in QA so you can administrate a superior model. Next thing you know, that’s a highlight on your CV that opens doors for you which would be closed to garden-variety film readers.

One of the most valuable things I learned from my current hacker-induced situation regarded a couple types of insurance (that I wish I had, and that I am now getting): “Business interruption” and “cyber” policies. Pretty straightforward—if your business gets disrupted, such a policy steps in and covers your lost revenue, potentially other things.

Straightforward, yes…but heretofore unknown to me. I’ve been mingling with other rads for over 20 years and not a single one ever said anything about it. (And shame on the various financial and insurance people who have been in my life to never have brought it up! Look how much premium-money they left on the table.)

Knowing of such things, what rad group wouldn’t be insured to protect itself from such circumstances? It wouldn’t be unreasonable for rads in the group to ask that their contracts specify a portion of such insurance-payouts be earmarked to safeguard their income. One might even argue that a group valuing its members should do so without being contractually obligated, but your mileage may vary.

Alternatively, a rad might just get his own coverage. My quick inquiry turned up a quote of under $2k for conjoined interruption/cyber policies…same ballpark as what I pay for disability insurance. An employer might be willing to simply add such a sum to your comp—paying for your coverage rather than committing to shield you with its own policy. And if one employer is willing to do absolutely nothing in this regard—well, others might do better, and more competitively attract rads as a result.

Did I spend time feeling sore about my situation before I learned about, and pursued my own, business-interruption and cyber policies? Sure; it’s only natural to have an emotional reaction when things don’t go your way. But I don’t let it paralyze me. Let it have its moment, then move on. Or, better yet, harness it—turn frustration/anger at misfortune into a drive to learn from it and take action.

It would be ideal to take stock of one’s most-used unhelpful “lessons.” Assemble them into a mental rogues’ gallery. Then, when one or more of them gets triggered by adversity, let it serve as an alarm clock: Wake up! There’s something good to be learned here, don’t miss it!

The tricky part is that the best learning opportunities arise in situations of adversity, when you’re most likely to be distracted.

Always Be Learning (the right stuff) 

Deep learning improved accuracy of diagnosing interstitial lung disease with a proposed content-based image retrieval of similar chest CT images in a recent study.

, included 288 patients with confirmed interstitial lung disease and available CT images.

Investigators used a proposed content-based image retrieval (CBIR) search engine to pull CT images from a database of these cases, which included usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia and chronic hypersensitivity pneumonitis.

Eighty of the cases were used as test cases and the CBIR pulled the top three similar CT images from the database using a deep learning algorithm that compared the extent and distribution of different disease patterns.

Investigators compared the diagnostic accuracy of eight physicians with varying levels of experience before and after the CBIR was applied.

“The proposed content-based image retrieval system for chest CT images by using deep learning can improve the diagnostic accuracy of interstitial lung disease (ILD) and interreader agreement in readers with different levels of experience,” the authors, led by Jooae Choe, MD, PhD, wrote. “This system can be expected to provide radiologic decision support to centers where thoracic imaging expertise is unavailable or cases of ILD are scarce.”

Overall, diagnostic accuracy improved from 46.1% before CBIR to 60.9% after CBIR. Diagnostic accuracy in UIP cases improved from 52.4% to 72.8%, and in NSIP cases from 42.9% to 61.6%.

CBIR also improved the agreement of CT interpretation between physicians (Fleiss k, 0.32 vs 0.47; P = .005).

Among non-radiology physicians, accuracy improved from 52% to 72% in cases with definite UIP CT pattern. For radiology residents, the change was 67% vs 85%.

“A major challenge of CBIR to be used as a decision-supporting tool is finding features and distance measures that match the clinicians’ requirements on the basis of a particular application,” the authors wrote. “By applying deep learning, structural characteristics (eg, reticulation and ground-glass opacity) inherent to a specific ILD pattern can be quantified on CT images with high accuracy and incorporated into CBIR.”

Mark O. Wielputz, MD, professor in the Department of Diagnostic and Interventional Radiology at Heidelberg University Medical Center, wrote in a related commentary that the database should be expanded into a continually growing system and multidisciplinary team discussion is still necessary for final diagnosis.

“Reference image retrieval is appealing because a decision regarding diagnosis is still made by a human reader provided with supporting image information,” Wielputz wrote. “This creates confidence in such systems compared with computational algorithms that provide only a list of differential diagnoses without explanation.”

He described the system as a shortcut in learning the patterns of diseases.

“A pattern-based and automated search should help radiologists and may be a game changer for computer-aided diagnosis,” Wielputz wrote.

A recent study showed how using artificial intelligence to retrieve reference images could improve diagnosis of interstitial lung disease with chest CT.

Deep Learning Can Improve the Diagnostic Accuracy of Interstitial Lung Disease 

Pranav Chitkara, M.D. is a Board Certified, Fellowship Trained Radiologist. Dr. Chitkara completed medical school at The University of Texas Southwestern and his radiology residency at State University of New York in Brooklyn. Dr. Chitkara also completed his fellowship in Musculoskeletal Imaging at Cleveland Clinic.

Young male with chest and right knee pain after injury

Case: Young male presents with chest and right knee pain post-injury. 

A young male presents with chest and right knee pain after injury. What most likely represents the imaging findings? ​ ​


Image IQ Quiz: Young Male With Chest and Right Knee Pain After Injury

Radiology has its ghosts. There’s ghosting-artifact (most commonly in MR, but the term has been used in other modalities). Ghost-reading: Someone without credentials or even a local license provides a low-cost interpretation, and then a legit rad signs that report with his own name. Plus various imaging-findings that got the word attached to them (“ghost meniscus,” “ghost vertebra,” etc.).

All of which might be interesting things, but not my focus today. Instead, another term that’s not specific to our field, but can be highly relevant: The social phenomenon of “ghosting.”

Most of us are familiar with the concept, even if we don’t know it by that name. I only learned this bit of slang a few years ago. It wouldn’t be the first time I’ve emerged from under a rock to join the rest of vernacular-society, but perhaps this column will serve to do the same for a few readers out there who are as unhip as I.

Ghosting refers to unilaterally ceasing communication with someone. Passively, without warning or declaration of intent. You just stop taking their calls, responding to messages, etc. You effectively become a ghost of the person they thought they had a relationship with. Most probably first experience this behavior during the breakup of a romance.

If you’re doing the ghosting, you might see nothing wrong with it. Or you feel some measure of guilt, like telling a “white lie.” Yes, maybe it would be nobler of you to stand up and tell the other party why you’re parting ways…but your reasons could be perceived as hurtful, might even spark a confrontation with worse consequences. Better to take the path of least resistance and hope they get the hint or have enough other stuff going on that they’ll hardly notice your absence.

Being ghosted, on the other hand, doesn’t leave you with much sympathy for your ghoster. Don’t they owe you more than that? Some measure of respect, an understanding of what you did (or didn’t do)? Or at least an “It’s not you, it’s me” story so you don’t indefinitely question yourself over the matter? It can be downright infuriating: They snuck away like a thief in the night, too lazy to give you the closure you deserved.

Folks might get introduced to ghosting during awkward breakups in gradeschool, but the practice of quietly retreating from an unwanted relationship is far more widespread. Healthcare included, despite what one might have hoped for regarding the behavior of “professionals.” Small wonder that the radiologist, a common poster-child for social ineptitude or avoidance, is no exception.

You might experience ghosting, for instance, if you’re supposedly in a partnership-track and ask about your status…but the current partners don’t intend to make you one (if they ever did). If the raise or end-of-year bonus you expected has failed to materialize. You might be ghosted by someone who’s not completing a task for you—a shared project, something you delegated, or even something you requested of a superior. Heck, you could be the one who completed the task, and whoever asked it of you might avoid responding because they don’t want to deal with the results.

You can be ghosted by folks with whom you have the most tenuous of connections; as long as there is a reasonable expectation of response, there’s the opportunity to ghost. I’ve previously written of the unfortunate prevalence of ghosting in the radiology-recruitment world: An awful lot of would-be employers fail to answer inquiries, leaving job-seekers in the dark about what happened to their applications: Being considered? Rejected? Lost in the ether?

Being ghosted professionally can be tougher to deal with than personally. At least with a vanishing social contact, you can work through your side of things and eventually shrug it off. Not so much if the individual ghosting you has something you want/need, or, worse, is someone you still have to work with; every time you see the perpetrator, it’s an annoying reminder. Especially if the ghost ever requests something of you.

Something I’ve come to understand in the fullness of time: Chase as few of those ghosts as you possibly can. It’s a waste of your time and energy, and even if you catch them it probably won’t be worthwhile. They’re scared of confronting you; in addition to whatever they originally didn’t want to discuss, if they face you now they do so as a coward. If you do wind up interacting, avoid referencing their ghosting-behavior (tempting as it may be to watch ‘em squirm more).

A ghost has tried in a very passive way to tell you “no” without actually saying the word. Trying to pry out a “yes” won’t generally get you what you want; they might speak affirmatively but their subsequent actions will rarely live up to it. They’ll do a halfhearted job if they do it at all. They might even resent you for cornering them, and find ways to punish you for it.

Accepting the unspoken “no” doesn’t get rid of all ghosts. Some come back to haunt you. Maybe testing the waters to see if they can continue the relationship without having to address whatever issue they were trying to avoid. Or maybe they just like playing head-games. I’d suggest keeping such folks at arm’s length; if they were comfortable ghosting you once, they’ll surely do it again. Why give them another chance to let you down?

 need that ghost to finish what it started, or you know that nobody else would fill its shoes as well. I’ve had success offering “bait” to come back. A useful bit of information, or some assistance I can provide. The sort of thing I can hold off on delivering, or parcel out, such that the ghost won’t get what it wants until it’s given me what I need. Not a maneuver you want to overuse: There are multiple ways it can go awry, and once a ghost learns you’ve got such tricks up your sleeve, it can become very circumspect about your offerings…or extra-clever about getting them without giving anything in return.

Chase as few of those ghosts as you can in an effort to save your time and energy.

Ghosts in Radiology

Assessing left atrium parameters through cardiac CTA before transcatheter aortic valve replacement may predict mortality in patients with severe aortic stenosis, a recent study found.

, included 175 patients with severe aortic stenosis who underwent cardiac CTA before transcatheter aortic valve replacement (TAVR). Reduced left atrium emptying fraction (LAEF) was associated with an increased risk of mortality within 24 months after TAVR.

“The CT scan performed routinely before every TAVR could be used more extensively than it is currently,” Gilberto Aquino, MD, of the Medical University of South Carolina told Diagnostic Imaging. “By extensively assessing a structure that is already included in the scan, such as the left atrium, we could improve pre-TAVR risk stratification and post-TAVR monitoring without added patient radiation exposure.”

The median age of study participants was 79 and 52.6% were men. Within 24 months after TAVR, 38 patients (21.7%) died. Investigators calculated maximum left atrium volumes (LAVImax) and minimum left atrium volumes (LAVImin). LAEF was automatically calculated. Cox regression was performed to determine which parameters were predictive of mortality.

Clinically, increased LAVImax and LAVImin and decreased LAEF -- most commonly assessed using transthoracic echocardiography -- are associated with a higher risk for heart failure, atrial fibrillation and stroke, the authors noted.

In univariable analysis, all three parameters were predictive of mortality. But only LAEF independently predicted mortality after adjusting for clinical parameters (hazard ratio 0.97 [0.94-0.99]; p=.02).

“We were surprised to find that only left atrial function, represented by left atrial emptying fraction (LAEF) in our study, was independently associated with mortality after TAVR, while volume parameters such as maximum and minimum left atrial volumes were not independently associated when adjusted for confounders,” Aquino said. “However, considering that our patient population had severe aortic stenosis, their baseline volumes might have been chronically increased at baseline, and thus, only functional impairment was able to effectively differentiate those at higher or lower risk for mortality after TAVR.”

The study evaluated left atrium parameters using predictive clinical variables and the Society of Thoracic Surgeons Predicted Risk for Mortality (STS-PROM). Adding the parameters to STS-PROM improved the ability of the model to predict mortality.

“The increasing popularity of TAVR should be met with improved risk stratification and post-treatment surveillance, and using a readily available CT scan beyond its typical use for valve sizing and peripheral access route evaluation could provide more information than is currently used in clinical practice,” Aquino said.

Limitations of the study include the limited temporal resolution of cardiac CTA-derived left atrium parameters. The advanced age of the participants also may have resulted in other causes of death confounding the assessment.

Ongoing advances in artificial intelligence-assisted assessment are anticipated.

“Ultimately, with the increasing frequency of TAVR, the goal is to maximize the use of imaging to improve pre-TAVR risk stratification by assessing multiple structures, such as the cardiac chambers, coronary arteries, pulmonary artery, muscle mass, and epicardial, subcutaneous and visceral fat,” Aquino said. “This could allow the creation of a comprehensive CT risk score for TAVR that would help determine which patients require closer monitoring.”

Cardiac CTA before transcatheter aortic valve replacement can be used to better determine risk by assessing left atrium functional parameters that are predictive of mortality.

Using Cardiac CTA Before TAVR to Assess Left Atrium Parameters May Predict Mortality

A couple Thursdays ago, I got an early-morning call from my radgroup: Don’t bother trying to log in for work, that day or indeed the next few. A nasty bit of hacking had occurred, and taken their entire computer-system down.

Working via teleradiology since 2011, and in facilities large and small before that, I’m no stranger to outages. Sometimes they’re a welcome break…especially if you’re salaried and getting paid the same whether you’re working all day or twiddling your thumbs. Not as much if you know that imaging-cases are still accumulating, and you’ll be frantically trying to catch up when your access to them is restored. 

 not if you’re “per click,” earning zippity-doo-dah while your employer pulls itself together.

Unlike other outages I’d seen, this one wasn’t over in a matter of hours, or even a couple of days. I still don’t know all the details; one of the other telerads theorized that it was on the level of ransomware/cyberterrorism, such that Federal entities were involved. Everything was down, even the group’s websites and email systems, and there was nary a guesstimate as to when we’d be back on our feet.

So far beyond the realm of anything that might have been planned for, this was tantamount to a “black swan” event for the group. I’ve referenced the term in this column before, and there’s a worthwhile book on the subject (“The Black Swan: The Impact of the Highly Improbable,” by Nassim Nicholas Taleb). I won’t rehash it here.

Just because you can’t predict such things, doesn’t mean you won’t have to deal with them if they happen. And even though you can’t thoroughly plan for the unknown (or it would be prohibitively costly to protect yourself against everything that’s less than 0.001% likely to ever occur), there are some things that can make you more resilient for when it comes busting down your door.

Developing and maintaining plans for more probable/predictable incidents is a good start. Some components of those plans might be useful when a “black swan” turns up. For instance, a contingency strategy dealing with more typical, briefer outages might provide a framework for when hackers hold your system hostage or terrorists detonate an EMP in the area. It’s a lot easier to modify a partially-relevant plan for your current situation than to create one from scratch.

Even if none of the plans you’ve formulated can be adapted, devising them in the first place has you in a better place than if you hadn’t. You (and, hopefully, your team) have already gone through the exercise of thinking about the various components that make your operation work, and what happens if one or more of them are taken out. What it might take to function in their absence, if any substitutes can be used, who to call for repairs/replacements, etc. The basic process of methodically sizing up a bad situation will come a lot more readily to you if you’re done it before.

Still, by its very nature a “black swan” event isn’t tidily handled by preparation. The best means of dealing with it is being ready to take, and roll with, its punches. Rather than panicking and going to pieces, you take stock of your troubles and what’s still intact. Then, you have an idea of what stands between your current situation and normalcy, and can start figuring how to get from hither to yon.

Facets of this need not be complex puzzles or massive undertakings. For instance, my radgroup’s usual means of communication (email and instant-message app) were down. Reestablishing contact began with simple phone-calls, followed by external email accounts. Those wanting to keep personal e-ddresses out of the mix simply created new Gmails for the purpose. Group-messages could then be used to relay marching orders and share updates as new information became available (also for offsite rads like myself to post questions).

Another important aspect of being able to roll with the punches is having flexibility and redundancy in your setup. If, under even the best of circumstances, your operation is stretched to its limit or liable to unravel if one tiny component falls out of place, it won’t take a black swan to create havoc…but such an event will be all the more devastating when it happens.

More robust by far is an organization whose members’ roles overlap. If one or more are taken out of commission for any reason (illness, equipment failure, whatever), others might have to stretch in compensation but the net is more capable of holding the burden than tearing under it. And if members of the team are used to having Operations “call an audible” to redirect their efforts when circumstances warrant, they’ll be that much more capable of shifting focus in times of crisis.

Individual members of the team can also make themselves more resilient for when black swans come a-calling. In a lot of ways, similar to the organizational-level stuff: Don’t panic, be ready to adapt as situations arise, etc. That’s especially important for per-click rads who might be facing a sudden cutoff of income for the duration of an outage. Having emergency funds in reserve, for instance, instead of living paycheck-to-paycheck. Some might even maintain separate sources of income, such as contacts with locums agencies.

Simply keeping an adaptive frame of mind can go a long way: Sure, it might be worrisome when your employer is down for the count and you don’t know when they’ll be back up. Maybe you nervously eyeball job-listings, just in case…but, in the meantime, you’ve suddenly got an unexpected bit of vacation. Even if it’s unpaid, try to make use and/or enjoyment of it. Sleep in, jumpstart your fitness regimen, get that stuff done around the house you’ve been putting off, meet up with some friends you haven’t seen.

That visit from the black swan, played the right way, might ultimately turn out to be something you look back on and realize you desperately needed.

Even though you can’t thoroughly plan for the unknown, there are some things that can make you more resilient for when it comes busting down your door.

Things Unforeseen

Dr. Gruen is the chief medical officer of IBM Watson Health Imaging.

If only we were as good at prevention and early detection as we are at treating complex disease in U.S. healthcare. Generally, people know what they should do, and in some cases, they do it. For example, many patients religiously go to the dentist for a cleaning every six months to prevent cavities. 

But screening women with consistent mammograms beginning at age 40 to detect breast cancer?

 Not so much. Just imagine how many lives we could save if we looked for breast cancer with the rigor and diligence with which we prevent tooth decay.

An Ounce of Prevention: Weighing Evidence and Personal Risk Factors

Unfortunately, when it comes to breast cancer screening recommendations,

 there are multiple sets of guidelines, many of them different. Conflicting recommendations can breed confusion and, far worse, can potentially threaten lives. One study estimated that if women followed less stringent recommendations and started screening at age 50 (instead of 40), and women aged 50-74 only screened every other year (instead of annually), it could result in about 6,500 more deaths from breast cancer every year in the United States.

To further complicate matters, even the best evidence-based guidelines may not be the best approach for every patient. All patients need to make sure their complete history is part of their healthcare decision making, and that is particularly important for breast cancer patients. Each woman has a unique personal and family history which may impact her risk of getting breast cancer. There are many factors that women and their physicians should consider when determining the best approach to breast cancer screening, such as personal health history, family history, genetic profile and hormonal considerations, among many others.

Doctors don’t know their patients as well as patients know themselves. For example, who in the family has had cancer, especially breast, ovarian or cancer of the gastrointestinal tract? Has the patient ever had a breast biopsy that showed abnormal cells, or prior cancer? At what age did she have her first period? Go through menopause? How many pregnancies? How dense is the patient’s breast tissue? Women and their doctors must work through these issues together to pursue more effective prevention.

Where Artificial Intelligence (AI) Can Help With Breast Cancer Prevention Efforts

As a breast radiologist, I believe one of the keys to better outcomes will be a more personalized level of risk assessment for every patient. AI can help deliver this personalization. For example, women with the densest breast tissue have a higher risk of breast cancer.

 These women may benefit from more extensive screenings, such as ultrasound or breast MRI. AI can help conduct density assessments to identify higher-risk patients.

In addition to density assessment, AI can enhance breast cancer detection by helping clinicians identify abnormalities on a mammogram, including a 3D (tomosynthesis) mammogram, that might be overlooked by even the most experienced radiologist. 

There is some promising research around applying data and AI to identifying breast cancer. An IBM research team analyzed de-identified mammography images, linked to comprehensive clinical data and biomarkers, to create a new algorithm designed to predict breast cancer.

 The combined machine- and deep-learning model correctly predicted the development of breast cancer in 87 percent of the cases it analyzed – an accuracy comparable to radiologists.

There are many exciting possibilities that could result from this type of research. For example, this may be the first study to incorporate both mammograms and electronic health record data for the prediction of breast cancer, which shows the potential of one day harnessing multiple data sets and AI algorithms to tailor risk assessment to each patient. The research team 

 analyzing clinical risk elements to better understand their relationship to an individual’s personalized risk.

To be clear, AI will never replace the radiologist. Another large-scale IBM Research study found that no single AI algorithm could outperform radiologists, but the combination of AI with radiologist assessment improved overall accuracy.

And no matter how good the technology gets, there will never be a replacement for good, patient-centered care. I encourage women to know their risk and talk to their doctors about breast cancer screenings. I’d like to see all patients do for the rest of our bodies what we so willingly do to prevent dental disease. The benefits to our lives can be dramatic.

This article does not provide medical advice. For information on the benefits of breast cancer screening starting at age 40, visit the 

For a more detailed discussion of differences between recommendations, see 

“Clearing up confusion over breast cancer screening recommendations”

Hendrick and Helvie, “United States Preventive Services Task Force Screening Mammography Recommendations: Science Ignored.” 

 2011;196: W112-W116. 10.2214/AJR.10.5609

Source: NIH National Cancer Institute. https://www.cancer.gov/types/breast/breast-changes/dense-breasts

Akselrod-Ballin A, Chorev M, Shoshan Y, et al. Predicting breast cancer by applying deep learning to linked health records and mammograms. 

Schaffter T, Buist DSM, Lee CI, et al. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. 

Artificial intelligence can help seek out and identify high-risk patients who might benefit from more extensive screening. 