Diagnostic Imaging brings you coverage of ARRS 2022 annual meeting.

Coverage of ARRS 2022 Annual Meeting

Mobile imaging has a variety of benefits for patient care. For example, consider the challenges of transporting critically ill patients for imaging. Possible complications of such transport can range from respiratory complications and loss of intravenous access) to traumatic injuries and infection.

 Bringing diagnostic imaging technology directly to the bedside eliminates the risks of patient transport. It also eliminates the need for transport staff, freeing them up to tend to other patients in the facility.

Eliminating transportation can also promote healing of the patient being imaged as well as other patients within the area. How? Consider the elimination of the noise and activity associated with having hospital staff prepare a patient for transfer, and the recurrence of that commotion when returning a patient to his or her room. Studies show that a quiet environment promotes sleep and rest, thus helping patients heal more quickly. Less noise could mean fewer spikes in heart rate, blood pressure, cortisol output and general anxiety. This holds true for nurses and other hospital staff as well.

To help promote rest, Carestream redesigned the 

 to make it quieter and less disruptive. Our engineers dampened the drive motors to make going in and out of patient rooms extremely quiet. Additionally, the brakes required to position the system are now practically inaudible. These enhancements reduce disruption to the patient receiving the exam as well as nearby patients.

Emphasizing Improvements to Mitigate Infection Risk

Infection control is always of concern within the health-care environment, and even more so today with the emergence of COVID-19. Imaging patients at their bedside can help reduce the spread of contagions that could occur during patient transportation.

The display screen on DRX-Revolution is flush-mounted, which helps protect against fluid ingress. The exterior of the mobile systems can be disinfected thoroughly and is ready to use again minutes after cleaning. In contrast, some imaging rooms require up to 45 minutes between exams due to mandated air exchanges.

Another driver for the increase in mobile imaging is its role in providing a quick diagnosis. A portable X-ray unit enables image capture at a patient’s bedside in much less time than it would take to transport a patient to an X-ray room. Images can be viewed by a physician immediately on the unit’s console or in the PACS.

This feature is beneficial to a larger part of the patient population beyond critically ill patients in the intensive care unit (ICU). For example, trauma patients with life-threatening injuries can be imaged right at the bedside rather instead of risking the complications of transport. Even patients with non-life-threatening injuries, such as broken bones, can be imaged at their bedside in the emergency room (ER).

Several of our customers utilize the DRX-Revolution during surgical procedures. Surgeons can immediately view images to assist in determining and documenting the alignment of fractures, placement of hardware, spinal level localization, presence of a foreign body, etc.

In all cases, faster radiology studies and quicker diagnoses can help treatment and healing begin sooner.

Completing examinations at the bedside also can help increase patient throughput. I have been a radiologic technologist for more than 20 years so I understand the need to balance the time it takes to properly capture an image with the need for increased throughput. I am really pleased with the enhanced workflow features on our Carestream mobile solutions that help streamline image capture.

For example, on the DRX-Revolution, the collimator knobsare now on both sides of the tube head, making them easily accessible in any situation and position. This is extremely helpful in tight and cluttered situations, such as the ICU and ER.

Our ImageView software minimizes screen transitions, so technologists have all the controls they require on a single screen. This makes setting up and completing examinations much quicker and efficient. Additionally, there is the ability to access the hospital information system (HIS)/radiology information system (RIS)directly from the desktop, allowing technologists to start and end patient exams without having to go to a second workstation.

The redesigned bin on the DRX-Revolution provides the ability to charge the detector, ensuring there is always a detector ready for use. And lastly, functional lighting helps alert users, both in and out of the room, of the status of the system.

All these features help the technologist complete the examination more efficiently, allowing him or her to focus more on the patient. This can improve satisfaction with the experience for both parties.

These strong benefits of mobile imaging will continue to increase its use for diagnostic radiology (DR) imaging. When quality images can be captured at the bedside, this can lead to increased patient comfort, reduced infection risk, less complications, and hopefully improved outcomes.

1. Knight PH, Maheshwari N, Hussain J, et al. Complications during intrahospital transport of critically ill patients: focus on risk identification and prevention. 

2. Romero MR, Brock M. HUSH initiative: a noise reduction project. Presented at 7th Annual Nurses Association’s Nursing Quality Conference, Atlanta, February 2013. Available at: https://www.researchgate.net/publication/267343280_HUSH_Initiative_A_Noise_Reduction_Project . Accessed May 26, 2022.

3. Algee B. Imaging innovation on the move: bedside imaging during COVID-19. Available at: https://www.youtube.com/watch?v=hU1G8Xa0Ejs . Published October 21, 2020. Accessed May 26, 2022.

Editor’s note: This article was adapted with permission from its original publication on Carestream’s Everything Rad blog at 

https://www.carestream.com/blog/2020/12/08/benefits-of-mobile-imaging/

Articles

As imaging volume continues to escalate for radiologists, more and more health-care facilities are turning to mobile imaging for increased efficiencies and benefits for patient care. 

Assessing the Benefits of Mobile X-Ray Imaging

Emerging research suggests that a deep radiomics model, which incorporates clinical, textural, and deep learning features, may enhance the diagnosis of osteoporosis on hip radiographs.

, researchers assessed the use of seven deep radiomics models for diagnosing osteoporosis in 444 patients who had hip or pelvic AP radiographs and dual-energy X-ray absorptiometry (DXA).

The study authors found that the deep radiomics model that combined deep learning features with clinical and textural features (model-DTC) had an area under the curve (AUC) of .95, 89 percent sensitivity and the highest specificity at 83 percent. In contrast, the model that only emphasized clinical features (model-C) had higher sensitivity (99 percent) but only 31 percent specificity and a lower AUC (.83). The model that only emphasized textural features (model-T) had a lower AUC (.77) and sensitivity (77 percent) and a 55 percent sensitivity rate, according to the study.

“Our study results showed that deep radiomics models can be used to diagnose osteoporosis on hip radiographs with high diagnostic performance … and demonstrate feasibility as an opportunistic diagnostic tool,” wrote Hye Jin Yoo, M.D., Ph.D., who is affiliated with the Department of Radiology at the Seoul National University College of Medicine in Korea, and colleagues.

According to the study, 356 of the patients were women and the mean age of the study population was 70. In addition to model-DTC, model-C and model-T, Yoo and colleagues assessed model-D (emphasizing deep learning features), model-TC (textural and clinical features), model-DC (deep learning and textural features) and model-DT (deep learning and textural features).

For the four models that incorporated deep learning features, the authors noted an AUC range between 0.92-0.95 in contrast to an AUC range between 0.77 to 0.83 for the three models without deep learning features. In the models without deep learning features, Yoo and colleagues noted the specificity rate ranged between 31 to 55 percent in comparison to a range of 71 to 83 percent for the models with deep learning features.

Given the retrospective nature of the study, the authors acknowledged the possibility of patient selection bias. They noted that their model may be limited when it comes to treatment monitoring as they did not incorporate regression methods that could facilitate continuous reflection of bone mineral density. The study authors also pointed out they did not assess the model for its ability to predict the risk of fragility fracture.

Deep radiomics models that included deep learning features had a 40 percent or greater increase in the specificity rate for diagnosing osteoporosis on hip radiographs in comparison to models that only emphasized clinical and/or textural features.

Study Looks at Deep Radiomics Approach for Diagnosing Osteoporosis on Hip X-Rays

Could an emerging artificial intelligence (AI)-enabled triage platform facilitate earlier detection and treatment for people who suffer a pulmonary embolism (PE)?

The Rapid PE Triage & Notification product has garnered 

510(k) clearance from the Food and Drug Administration (FDA)

, according to the product’s manufacturer RapidAI.

AI-enabled platform reportedly offers a variety of key benefits

• Enhanced identification of suspected central PE on computed tomography pulmonary angiogram (CTPA) images

• Real-time notification with previews of CTPA imaging available for review via mobile devices, desktop computers and laptops

• Streamlined communication among interventional teams to facilitate triage of priority cases and timely treatment

“I’m excited to see firsthand how artificial intelligence will transform the way we triage and manage PE, a historically complicated process involving a diverse care team,” noted Jimmy Kerrigan, MD, FSCAI, FACC, RPVI, an interventional cardiologist affiliated with Ascension Saint Thomas Heart at multiple locations in Tennessee. “After seeing what RapidAI technology has done for stroke, I’m optimistic about about its potential to optimize our care for patients and to hopefully improve patient outcomes.”

The new artificial intelligence-powered software reportedly helps detect central pulmonary embolism (PE) on computed tomography pulmonary angiogram (CTPA) images and streamlines communication among interventional teams to bolster treatment outcomes.

PE Triage Platform from RapidAI Receives FDA 510(k) Clearance

A large retrospective study has found no difference between digital mammography and digital breast tomosynthesis when it comes to cancer detection in patients who had BI-RADS 4 breast lesions.

, researchers assessed 11,657 patients who were diagnosed with BI-RADS 4 breast lesions. According to the study, 6,020 patients were diagnosed with two-dimensional digital mammography (DM) and 5,637 patients were diagnosed with digital breast tomography (DBT).

While breast lesions were classified as BI-RADS 4 5.66 percent more often with DBT, the study authors noted similar cancer detection rates for DM (112.65) and DBT (120.76). In regard to biopsy results, there were 4,969 biopsies in the DM group and 4,452 biopsies in the DBT group. The study showed similar malignancy detection with 716 cases in the DM group (14.41 percent) and 712 cases in the DBT group (15.99 percent). There was also no evidence that DBT reduced unnecessary biopsies, according to the study.

The study authors acknowledge that DBT offers better imaging of masses, asymmetries, and other anomalies in contrast to DM. However, they maintained that the study revealed no significant differences between the screening modalities on a variety of measures among patients with BI-RADS 4 breast lesions.

“Our findings indicate that once the assessment is made as BI-RADS 4, biopsy outcomes were comparable for both DM and DBT as malignancy rates, cancer detection rates, and biopsy-derived positive predictive values were similar, such that differences were not statistically significant,” wrote co-author Stephen Wong, Ph.D., the John S. Dunn Presidential Distinguished Chair and chief research informatics officer with the Houston Methodist Cancer Center at the Houston Methodist Hospital, and colleagues.

While the study data was drawn from multiple hospitals, clinics and outpatient breast imaging centers, the research was done within a single health-care system, so the study authors concede a potential for bias. They also noted a possible limitation to the data due to BI-RADS 4 sub-categories not being included in hospital mammogram reports. Limitations with the data also prevented assessment of the impact of breast fibrodensity or tumor grading, according to the study authors.

In a study of over 11,500 patients who had BI-RADS 4 breast lesions with no prior history of breast cancer, researchers found no statistically significant differences between digital mammography and digital breast tomosynthesis in cancer detection rates or biopsy-derived positive predictive value.

Study Finds No Difference Between Digital Breast Tomosynthesis and Digital Mammography in Cancer Detection for BI-RADS 4 Lesions 

Emerging research suggests that artificial intelligence (AI) can improve the assessment of indeterminate pulmonary nodules on chest computed tomography (CT) exams.

In a recently published retrospective study in Radiology

, researchers examined the use of an AI-enabled, computer-aided diagnosis (CAD) software (Virtual Nodule Clinic, Optellum) for helping to determine the malignancy risk of indeterminate pulmonary nodules on chest CT. Six radiologists and six pulmonologists reviewed chest CT exams in 300 patients (mean age of 65) with pulmonary nodules ranging between 5 to 30 mm in size.

The researchers found that use of the CAD software improved the average sensitivity (from 52.6 percent to 63.1 percent) and specificity (87.3 percent to 89.9 percent) for high-risk nodules (malignancy risk threshold of 65 percent). For low-risk nodules (malignancy risk threshold of 5 percent), the study authors also noted improved sensitivity (94.1 percent to 97.9 percent) and specificity (37.4 percent to 42.3 percent) with the AI-powered software.

“The readers judge malignant or benign with a reasonable level of accuracy based on imaging itself, but when you combine their clinical interpretation with the AI algorithm, the accuracy level improves significantly,” noted study co-author Anil Vachani, M.D. MSCE, the director of the Lung Nodule Program and co-director of the Lung Cancer Screening Program at the Perelman School of Medicine at the University of Pennsylvania. “The level of improvement suggests that this tool has the potential to change how we judge cancer versus benign and hopefully improve how we manage patients.”

Use of the CAD software also improved interobserver agreement for the diagnosis of low-risk nodules by 21 percent and high-risk nodules by 17 percent, according to the study. Researchers also noted an overall increase of eight percent for interobserver agreement on management recommendations for indeterminate pulmonary nodules.

, Masahiro Yanagawa, M.D., Ph.D. said the improved assessment of indeterminate pulmonary nodules and improved agreement among clinicians on low- and high-risk nodules are promising.

“These results are crucial for brining AI-based tools closer to clinical implementation for risk stratification and patient management,” wrote Dr. Yanagawa, an associate professor at the Graduate School of Medicine/Faculty of Medicine at Osaka University in Japan.

Dr. Yanagawa also noted that future prospective research is necessary to validate the results.

“The evaluation of subsolid nodules as well as ground-glass nodules (which change little over time) will be an important area for future validation of the AI system,” added Dr. Yanagawa.

One factor limiting the application of results to a clinical setting was the small number of partially solid nodules in the study, according to Vachani and colleagues. While the intent of the study was to determine the impact of the AI-enabled CAD in assessing risk based on nodule characteristics, the authors noted that not supplying patient clinical information to those reading the CT exams limits general application of the study results to a clinical setting.

Prior to interpreting the chest CT images, the study authors noted the readers were informed that the prevalence of malignancy in the pulmonary nodules being assessed was higher than what one would normally see in clinical practice, and this may have led to inflated risk estimates in the study.

A new study revealed that an emerging artificial intelligence (AI)-enabled software tool led to improved sensitivity, specificity and inter-observer agreement for the diagnosis of indeterminate pulmonary nodules on chest computed tomography (CT) scans.

AI May Bolster Assessment of Pulmonary Nodules on Chest CT

Has digital breast tomosynthesis (DBT) lived up to the initial hype when it comes to breast cancer detection?

In her recent lecture on DBT at the Society of Breast Imaging (SBI)/American College of Radiology (ACR) conference, Debra Monticciolo, MD, FACR, FSBI, said DBT and two-dimensional (2D) digital mammography (DM) “are closer cousins than we expected.”

In five European studies comparing DM/DBT to DM that were published prior to 2018, the cancer detection rate (CDR) was incrementally better than that of DM, but Dr. Monticciolo said there are key caveats to consider with these studies. These studies emphasized biennial screening, which increases CDR and most of these studies had an older population (over 50 years of age) with a higher underlying prevalence of disease, which increases CDR, pointed out Dr. Monticciolo, the Dr. Robert and Alma Moreton Centennial Chair in Radiology and a professor of radiology and vice-chair for research with Baylor Scott and White Health, Texas A&M University.

Dr. Monticciolo said the American retrospective studies (emphasizing annual screening) prior to 2018 showed variable results in comparing CDRs between DBT and DM. Of the subsequent retrospective American studies published in 2018 and beyond comparing DM/DBT and DM, “there is no statistically significant difference in many of the studies for CDR,” noted Dr. Monticciolo, a past president of the American College of Radiology and the Society of Breast Imaging.

There is also conflicting data when it comes to comparing recall rates between DM/DBT and DM, according to Dr. Monticciolo. She noted that DM/DBT had a 4.14 average recall rate in five European studies published prior to 2018 and DM had a 3.58 average recall rate. Yet in U.S. retrospective studies prior to 2018, the average recall rate was 8.0 for DM/DBT and 11.3 for DM. However, in seven recent studies published in 2018 and beyond, Dr. Monticciolo pointed to improved recall rates with DBT, noting average recall rates of 7.72 for DBT and 9.0 for DM.

In her SBI/ACR lecture, Dr. Monticciolo also looked at the viability of synthesized mammography (SM) in possibly replacing the use of DM in DM/DBT. While the use of SM reduces image acquisition and the combination of SM/DBT reportedly reduces radiation dose by approximately 40 percent in comparison to DM/DBT, Dr. Monticciolo said there has a been a slow adoption of SM among radiologists since the availability of the technology in 2013. She noted one 2017 study of over 1,150 breast radiologists found that approximately 75 percent had SM but utilized DM/DBT.

This boils down to inferior spatial resolution for SM, according to Dr. Monticciolo, the section chief of breast imaging at Baylor Scott and White Healthcare, Texas A&M University. She said SM accentuates high contrast objects, but problems arise at points close to resolution threshold.

“There isn’t a breast imager anywhere who would rather read (a SM image) than a standard DM view,” maintained Dr. Monticciolo. “ … There is no question that SM is an inferior image.”

She noted that a 2021 meta-analysis compared SM/DBT versus DM and DM.DBT. In 11 studies that compared SM/DBT and DM, the use of SM/DBT resulted in a higher CDR.Yet in seven studies that compared SM/DBT with DM/DBT, there was no difference in CDR, according to Dr. Monticciolo. She noted the authors of the meta-analysis found that SM/DBT screening offered similar detection rates for all cancer, invasive cancer, and ductal carcinoma in situ (DCIS) in comparison to DM/DBT as well as a lower biopsy rate and recall rate.

Dr. Monticciolo said three other meta-analyses published in 2021 found equivalent CDR between SM/DBT and DM/DBT.

She added that the recently published STORM-2 trial found that SM/DBT and DM/DBT were equivalent in regard to CDR, and both had a higher CDR than DM. However, while these findings held up for patients 60 years of age and younger, “the (CDR) advantage of SM/DBT disappears and the advantage of DM/DBT narrows” for patients over the age of 60,” noted Dr. Monticciolo.

“If you’re doing SM/DBT, you can feel good because the outcomes are probably going to be equivalent with DM/DBT,” summarized Dr. Monticciolo. “If you’re using DM/DBT, you can feel good because you are giving your patient the highest resolution exam that you can and as long as you stay within the federal (radiation) exposure limits, it’s completely safe.”

A closer look at the literature suggests the combination of digital breast tomosynthesis (DBT) and two-dimensional (2D) digital mammography (DM) has relatively equivalent cancer detection rates as using DM alone, according to a recent presentation at the Society of Breast Imaging (SBI)/American College of Radiology (ACR) conference.

Is Digital Breast Tomosynthesis Superior to Digital Mammography?

Eric Postal, MD, is a diagnostic radiologist with the University of Pittsburgh Medical Center. 

I am a RVU generator. It is not exactly how I envisioned my career shaping up. Sure, as a diagnostic radiologist, I expected much of my professional value to be expressible in terms of case volume. However, I also thought I would be doing substantial amounts of “on the clock” stuff away from the workstation, whether it was planning strategy, optimizing group efficiency, and even forming policy. In other words, there is an interest in “bigger picture” kinds of stuff.

There are various reasons why that hasn’t happened yet. In the meantime, if I want to philosophize about the greater meaning of my work, it’s going to be in terms of lives I have saved, improved, or at least prevented from getting worse as result of cases I have read.

In the abstract, this is not a hard concept to grasp. Rads can interpret some pretty complex imaging, to say nothing of how we manage when we are given less than ideal tools to work with (technically limited studies, poorly chosen protocols or even modalities for the clinical scenario, irrelevant/wrong/non-existent histories, etc.). Functioning as the “doctor’s doctor,” how could we not be doing important work?

Start trying to quantify it, though, and one rapidly gets brought back to Earth. Muddling through a typical worklist, it is very easy to wind up with a “how much of what I’m doing really makes a difference” feeling. Plow through a few dozen portable ICU chest images, for instance, endlessly describing a bunch of tubes and lines and how one can’t rule out edema on this one or pneumonia on that one. Heck, it seems like they order daily chest films in the ICU without waiting to see what we had to say about the previous films.

Maybe you do a lot of mammos and every so often, somebody publishes a study about how screening doesn’t have a statistical benefit on survival across the population. Perhaps you cover ERs, see trauma pan-scans on patients wrecking their ATVs and realize the same patients trashed their snowmobiles last winter. Did it matter that you identified their rib fractures last year when they just went out and broke some new ones now?

Then there’s the matter of rads not being the only ones looking at imaging studies. There is an assortment of jacks-of-all-trade (ER docs, PAs, NPs) glancing at X-rays, sonographers essentially giving prelims on their scans to whomever asks, and subspecialists like orthopedic surgeons or neurosurgeons proudly boasting that they read their subspecialty images better than we do. It is easy to come away with the sense that even if we didn’t make some key diagnosis (or rule it out), others would.

More than once in a blue moon, I’ve had the notion that, even with all this going on, even with the ever-increasing volume of unnecessary imaging that gets thrown our way to dilute things, there are times where we stand between good and bad outcomes. In our diagnostic rad roles, we have the chance to do what nobody else can or will. As an individual trying to distinguish himself, I may sometimes do what other rads wouldn’t.

Such moments, as rare as they might be in this sea of other factors, are our “delta.” It is the difference that we can make because of how we perform when we’re doing our thing.

Of course, we don’t knowwhen those moments are afoot. If I catch a non-obvious fracture, I don’t know until after the fact whether the ER staff noticed it or sent the patient home to follow up with their primary care doc. If I think I see a suspicious density on a mammo and suggest diagnostic workup or biopsy, I won’t know until afterward if I was sounding the alarm over something benign or malignant. Even if it was bad news, I would have to be omniscient to know whether I made a difference in the quality/quantity of that patient’s life. Maybe the tumor would have been slow growing and instead the patient dies earlier from complications of the chemotherapy that I helped make happen.

It’s only with a very indirect, been doing this for decades, instinctive kind of feel that I have come to sense what my delta might be. I try to underestimate it in order to keep myself humble and my expectations low.

For instance, I think it’s a reasonable hope that as I do my best work, perhaps there will be one case in 10 in which I might say something more helpfully than another rad might have (or refrain from saying something unhelpful). Meanwhile, if you take the population of rads as a whole, it seems safe to say that in at least one out of 10 cases, we produce better outcomes than some other doc would have (ER denizens and surgical subspecialists alike).

Of course, no matter who ultimately looks at a case, let’s keep in mind that an awful lot of imaging doesn’t contain actionable results relevant to the patient who got imaged. Either the imaging was a “fishing expedition,” it was the wrong type of study to order for the clinical interest, or no imaging was warranted in the first place. Assigning that a one in 10 probability might seem cynical to some but not to anybody whose worklists resemble those I have seen.

So, I’m looking at 10 percent for me versus other rads, 10 percent for rads versus other docs, and 10 percent for imaging studies overall. I can reasonably expect my delta to be at least one case in 10,000. My pace of reading cases has varied with the types of infrastructure offered by my last couple of jobs, but that translates to at least twice per year.

Does that seem rare? Of course, I’d like it to be greater, but I did set out intending to underestimate. Even going with two lives per year, that will turn out to be 34 since fellowship. Add another 20 for each decade I keep plugging away before I call it quits (or it gets called quits for me). That’s a few dozen folks who got to live longer and/or better purely because I was involved in their care.

I would consider that a decent accomplishment for my career.

Amid a glut of unnecessary imaging requests, common worklist frustrations and perceptions from other clinicians that they can interpret medical images just as well, recognizing your role as a humble difference maker may provide the ultimate RVU for patients.

What is Your Radiology Delta?

's Weekly Scan is your chance to catch up on the latest radiology content, from new research and device news to the latest case quizzes and conference reports. Review the slideshow below to see this week's top content:

Diagnostic Imaging's Weekly Scan: May 15-May 21

Slideshows

Catch up on the top radiology content of the past week.

In sharp contrast to previous reporting of side effects from clinical trials of COVID-19 vaccines, new findings from breast ultrasound research revealed a 

significantly longer duration of axillary lymphadenopathy among people who had COVID-19 vaccination

The retrospective study, which was recently published in the 

, involved 111 patients (mean age of 52) who developed unilateral axillary lymphadenopathy within eight weeks after having the Pfizer or Moderna COVID-19 vaccine. Initial breast ultrasound examination revealed a 4.7 mm mean cortical thickness of the largest axillary lymph node. Patients had subsequent ultrasound exams at four- to 12-week intervals until there was resolution of the condition, according to the study.

Researchers found that the mean resolution of axillary lymphadenopathy occurred 97 days after the initial ultrasound exam and 127 days after initial administration of COVID-19 vaccination. For the 60 patients who had the Pfizer COVID-19 vaccine, the mean resolution of axillary lymphadenopathy since the first vaccine dose took 117 days in comparison to 139 days since the first dose for the 51 patients who had the Moderna COVID-19 vaccine.

 Previous clinical trials for COVID-19 vaccines had noted an average one to 10-day duration of axillary lymphadenopathy.

“The prolonged resolution time supports a follow-up interval of at least 12 weeks for suspected vaccine-related lymphadenopathy and avoidance of delaying screening mammography after vaccination,” wrote Michele B. Drotman, M.D., chief of the Division of Breast Imaging and associate professor of clinical radiology at Weill Cornell Medical College, and colleagues.

In order to address patient anxiety and help prevent overdiagnosis of the increased frequency of unilateral axillary lymphadenopathy during the initial vaccine rollout, the Society of Breast Imaging (SBI) initially recommended a delay of breast screening exams until four to six weeks after a second dose of a COVID-19 vaccine. In 2021, the SBI also recommended short-term imaging follow-up at four to 12 weeks for unilateral axillary lymphadenopathy that was suspected to be vaccine-related. In March 2022, the SBI issued an updated recommendation of 12 or more weeks for follow-up imaging, a change that appears to align with the findings by Dr. Drotman and colleagues.

“The presence of subclinical lymphadenopathy and the long resolution time of lymphadenopathy, as observed in this study, should reassure radiologists and patients when lymph nodes suspected to be vaccine-related persist over multiple visits,” emphasized Dr. Drotman and colleagues.

Other predictive factors of a longer duration of axillary lymphadenopathy included greater cortical thickness on the initial ultrasound exam and having the second vaccine dose after initial ultrasound examination, according to the study.

The study authors acknowledged the potential for selection bias in the retrospective single-center study. Determining a more precise time for resolution of the lymphadenopathy was hampered by variability in the time between follow-up ultrasound exams and subsequent four to 12-week follow-up intervals, according to Dr. Drotman and colleagues. They also noted that patients with concurrent breast cancer were excluded from this study and that unilateral lymphadenopathy requires careful assessment in this patient population.

1. Lane EG, Eisen CS, Drotman MB, et al. Time for resolution of COVID-19 vaccine-related lymphadenopathy and associated factors. 

 2022 May 18. doi: 10.2214/AJR.22.27687. Online ahead of print.

2. Local reactions, systemic reactions, adverse events, and serious adverse events: Moderna COVID-19 vaccine. Centers for Disease Control and Prevention. Available at: 

https://www.cdc.gov/vaccines/covid-19/info-by-product/moderna/reactogenicity.html

3. Local reactions, systemic reactions, adverse events, and serious adverse events: Pfizer COVID-19 vaccine. Centers for Disease Control and Prevention. Available at: 

https://www.cdc.gov/vaccines/covid-19/info-by-product/pfizer/reactogenicity.html

While COVID-19 vaccine clinical trials noted that post-vaccine axillary lymphadenopathy lasted for one to 10 days, emerging breast ultrasound research found a mean resolution of 127 days after a first vaccine dose.

New Study Points to Longer Duration of Axillary Lymphadenopathy After COVID-19 Vaccine

Perhaps the biggest challenge in radiology right now is the current global shortage of iodinated contrast media (ICM), which is most commonly utilized for computed tomography (CT) exams. With the temporary shortage expected to last until the end of June, radiology departments and larger medical facilities are scrambling to address this issue, which can have a significant impact on patient care.

With this in mind, we recently spoke with two professors from the Yale School of Medicine: Dr. Joseph Cavallo, an Assistant Professor of Radiology and Biomedical Imaging, and Dr. Jay Pahade, an Associate Professor and Vice-Chair of Quality and Safety for Radiology and Biomedical Imaging.

co-authored an article on strategies that imaging facilities may take to address this acute shortage of iodinated contrast media.

 They outline three central strategies including ICM dose reduction, utilization of alternative imaging modalities and possible deferment of imaging deemed to be non-urgent in nature.

Dr. Pahade called ICM a mission critical agent.

“Most institutions rely heavily on contrast-enhanced CT for the management of their patients both inside and outside of the emergency department (ED). Almost every critical diagnosis in medicine has some arm tied to CT imaging in one fashion or another,” emphasized Dr. Pahade.

For additional insights on the shortage of iodinated contrast media, watch the video below.

Videos

In a recent video interview, two radiology professors from the Yale School of Medicine discussed the impact of the iodinated contrast media (ICM) shortage and potential strategies, ranging from ICM dose reduction to possible deferment of non-urgent imaging, that may provide a viable path moving forward. 