Diagnostic Imaging brings you daily coverage of ARRS 2021 virtual annual meeting.

ARRS 2021 Virtual Annual Meeting: April 18 to April 22, 2021

Non-contrast pituitary MRI offers an equivalent diagnosis to standard contrast-enhanced protocols when imaging for central precocious puberty (CPP), growth hormone deficiency (GHD), and short stature (SS).

In an award-winning poster presented during the ARRS 2021 Virtual Annual Meeting, Jennifer Huang, M.D., a second-year medical resident at Vanderbilt University, showed that using a non-contrast protocol could impact patient care.

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“Microadenomas, a common justification for contrast administration, may not influence management in this patient population,” she said. “Minimal inconvenience would be added for the few patients who would need to return for contrast-enhanced MRI for definitive diagnosis.”

For this study, Huang’s team conducted a retrospective review of 448 pediatric pituitary MRI studies – from patients with an average age of 8.2 years – captured between 2010 and 2019 for CPP, GHD, and SS. Two sub-specialty-trained pediatric neuroradiologists performed a blinded review of the non-contrast images.

Of the MRIs, 35 percent were for CPP, 49 percent were for GHD, and 16 percent were for SS. Gadolinium contrast was used in all 448 MRI scans, and 226 (50 percent) required sedation. Based on the team’s analysis 136 scans (30 percent) were abnormal – 71 percent of which (97 scans) agreed with original reports on blinded review of non-contrast images. And, 79 percent of the 39 scans that showed a discrepancy were of no clinical significance.

When the discrepancies were examined further, one was of uncertain significance, and seven (five extrasellar masses and two pituitary stalk abnormalities) were visible on non-contrast images, but they needed contrast to achieve definitive characterization.

Overall, Huang said, these results showed that opting for non-contrast imaging can provide benefit to patients.

“In a large majority of patients, the shorter non-contrast examination would provide savings in time, anesthesia, gadolinium, and associated costs,” the team said.

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The diagnostic performance for central precocious puberty, growth hormone deficiency, and short stature is similar to that of standard contrast protocols.

Non-Contrast MRI Diagnostically Similar to Standard Protocol for Pediatric Pituitary Pathologies

A change in radiology residency scorings is likely on the horizon as the United States Medical Licensing Examination (USMLE) Step 1 transitions from a numerical score to pass/fail.

In a poster presented during the 2021 ARRS Virtual Annual Meeting, Rebecca Zhang, a medical student at the University of Maryland School of Medicine, explained that radiology residency program directors will most likely begin to rely on USMLE Step 2 Clinical Knowledge (CK) scores as early as January 2022. These scores are considered an objective and standardized metric for screening applicants.

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The timing of when these scores will be needed, though, remains in question.

“Program directors remain unsure whether they will require Step 2 CK scores at the time of application or before interviewing applicant,” she said.

Zhang’s team conducted a 14-item anonymous, voluntary electronic survey of 308 active members of the Association of Program Directors in Radiology to determine the importance and potential impact of this pivot to pass/fail. They also performed a secondary analysis that compared responses based on the current Step 1 scoring screen.

Of those survey, 29 percent responded, and 64 percent said an applicant’s Step 2 CK score will likely rank as one of the top three factors in evaluating applicants. The score was followed by class ranking (51 percent) and the Medical Student Performance Evaluation or Dean’s Letter (37 percent).

In addition, 90 percent of respondents predicted their programs may or will require the Step 2 scores before application review with 50 percent anticipating the scores will be required after interview invitations are extended.

More research could potentially help identify other objective metrics for more holistic evaluations, Zhang’s team said.

“These results did not significantly differ between programs who currently use a Step 1 scoring screen and those who do not,” they said.

The switch to using USMLE Step 2 Clinical Knowledge scores is anticipated in January 2022. 

Pass/Fail Medical Licensing Exam Scores Anticipated for Residency Applications

Chest X-ray can prospectively predict outcomes for a patient with COVID-19 in high pre-test probability settings or a quick turnaround of RT-PCR testing.

In a poster presented during the 2021 ARRS Virtual Annual Meeting, Russel Reeves, M.D., a radiology resident at Thomas Jefferson University in Philadelphia, described how this type of imaging can potentially help providers prepare for the type of care a patient will need.

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“We developed an accurate and reliable tool for classifying COVID-19 severity, which can be used both at the attending chest radiologist and junior resident level,” he said. “This study identifies the laboratory, clinical and radiographic data that predict important patient outcomes, such as death, intubation, and the need for chronic renal replacement therapy (CRRT).”

For their study, Reeves’ team identified 240 patients admitted to an urban multi-center health system between March 16, 2020, and April 13, 2020, who had RT-PCR-confirmed COVID-19, including 142 males and 98 females with an average age of 65.

Chest X-rays captured at the time of admission were evaluated by three cardiothoracic radiologists and three diagnostic radiology residents who scored the images based on extent and severity of COVID-19 pneumonia. In addition, they also collected information on demographics, clinical characteristics, as well as admission laboratory values and assessed the inter-rater reliability among attending providers, residents, and the combined group of readers.

Based on their analysis, inter-rater reliability for chest X-ray scoring was high among the attending, residents, and combination group (a 0.686 average). Their evaluation showed no difference in outcomes for gender, race, ethnicity, or among those who had lung cancer or chronic obstructive pulmonary disease histories.

The team did note that even though disease severity seen on chest X-ray was an independent predictor of death, it was a non-independent predictor of CRRT and intubation – but not extracorporeal membrane oxygenation.

These results, the team said, add to the existing knowledge base around the role chest X-ray can play in COVID-19 care.

“These findings are supported by prior work, validating the utility of [chest X-ray] for patient prognostication, while adding new insight into COVID-19 infection during the initial presentation,” they said.

In high pre-test probability and quick RT-PCR settings, chest X-ray scoring can be predictive. 

Chest X-ray Can Predict Outcomes for Patients With COVID-19

The push toward greater utilization of artificial intelligence in radiology (AI) shows no signs of slowing, so it would be a good idea to brush up on the legal aspects of using it, a group of industry experts said.

In an April 9 perspective article published in 

 legal and radiology experts from Harvard noted that the use of AI in diagnostic imaging is largely uncharted territory. Some risks to providers, practices, and technology developers have been identified, but more could be revealed over time.

To help you prepare, H. Benjamin Harvey, M.D., J.D., a neuroradiologist from Massachusetts General Hospital, and Vrushab Gowda, J.D., a Harvard Law School graduate who is also pursuing his medical degree from the University of North Carolina at Chapel Hill School of Medicine, discussed some specifics of the liabilities around AI use in musculoskeletal imaging.

These cases typically deal with medical malpractice, Harvey and Gowda said. While plaintiffs have the burden of showing four distinct occurrences of negligence, courts will also rely on professional society standards, as well as community practice and department protocols. Consequently, it is vital to create guidelines, complete sufficient user training, and correctly integration AI.

In addition, AI is extremely helpful, but if you rely on it alone – rather than using the tool and deciding to dismiss its findings and recommendations – your malpractice risk will be greater, they said.

When it comes to using AI with medical imaging, there are two main concerns, they said – do you let your patients know you’re using AI, and if you do, what do you need to tell them?

Given that AI is largely used for research investigations currently, this issue hasn’t crept up much. But, once the tools are implemented clinically, the story will be different, especially if the technology eventually becomes autonomous further downstream.

Ultimately, Harvey and Gowda said, proper handling of informed consent will likely require a patient-based or provider-based standard, and the difference between them can be unclear.

“The rub of the matter is that radiologists should be cognizant of their peers’ disclosures and anticipate the sorts of information patients would find important when deploying AI,” they said.

When mistakes happen, patients are going to look for someone to hold accountable, they said. If you’ve used an AI tool that results in a negative outcome, your hospital or radiology practice could end up as the defendant – not you.

This situation of “vicarious liability” can be sticky, they explained, because it puts your practice or facility leadership in the hot seat. Consequently, there is a need for clearly written protocols on how to handle these potential situations.

You aren’t the only one subjecting yourself to liability and risk when you use an AI tool. The developer also bears responsibility for when things go wrong, such as mislabeling findings or recommending a biopsy – or worse, not recommending one.

Under a strict liability framework, if a plaintiff can prove that the mistake is a result of a manufacturing problem – which could be tough to do – they could win their case. Conversely, negligence principles call for examining the features of the healthcare facility rather the product.

Either way, they said, radiologists need to be aware about these possibilities and stay current with what’s going on.

With a lack of clarity around liability and responsibility with the future use of artificial intelligence, Harvey and Gowda both said, as radiologists, you must make sure your voice is part of the discussion around developing stringent AI imaging standards.

“Looking forward, imaging departments should articulate clear protocols for their use, to include procedures in the event of human-AI discrepancy,” they said. “They can be aided by [American College of Radiology]- and [Society of Skeletal Radiology]-validated guidelines, training programs, and model practices for deploying AI technologies.”

Liability and responsibility concerns are not significant with AI use now, but as the tools enter clinical use, that will change. 



4 Must-Knows about Future Radiologist Liabilities with AI

Currently, radiology practices face several challenges, such as decreased utilization during COVID-19, payer reimbursement policy changes, and consumerization of healthcare. A decrease in utilization has led to a reduction in staffing levels while payer reimbursement changes, such as increased prior authorization requirements, have added more work to the revenue cycle staff.

A recent statistic shows a 300-percent increase in previous authorization denials. Also, consumerization has required practices to overhaul their patient financial experience. Now more than ever, practices are required to adopt technologies like artificial intelligence (AI), machine learning (ML), and consumer-centric workflows to enable digital transformation in their workforce and practice.

Due to consumer expectations, there is a continuous shift toward digitally guiding the patient's financial experience. This, coupled with growing patient responsibilities due to high deductible plans, requires a new level of resources and focused efforts. Additionally, the lack of integrated technology between billing and electronic health record systems adds another layer of complexity to the existing revenue cycle workflow.

Revenue Cycle Management for the Pandemic

As imaging and radiology organizations continue to face increasing challenges in the financial exchange and reimbursement landscape, they are left with two distinct choices of adding more resources or automating key processes. It is crucial to adopt critical technologies capable of boosting efficiency throughout the revenue cycle. This allows employees to focus their time on value-added activities while addressing the historical challenges listed above.

After a year of reduced revenues due to the coronavirus, organizations are re-examining their administrative procedures to optimize efficiency and accuracy. With innovations, such as robotic process automation (RPA) and ML, organizations can improve the bottom line while scaling resources to focus on complex, value-added activities. Automating portions of the revenue cycle management system to a digital platform creates an opportunity for imaging/radiology groups to streamline even the most complex end-to-end processes and functions, allowing them to recapture lost revenue.

For example, a radiology group facing an increase in account receivables (AR) may begin to explore initial steps to automate several repetitive or mundane tasks to gain the most significant efficiency for improvements to their bottom line. Apart from the increase in AR, the radiology group would also face an average of a seven-day delay-in-claim submission.

With the implementation of ML algorithms, the radiology group can recognize patterns and suggest predictive actions for optimal claim follow-up and denial resolution. Predictive denial modeling and reporting can be performed in advance of the payor’s response. These cases suggest the correct fix for the denials using the past learning and underpayment analysis for out-of-network claims. Combining innovations, such as RPA and ML, makes it possible to automate even the most complex end-to-end processes and functions.

Due to the implementation of technology across the revenue cycle processes, a radiology group can successfully reduce a more than 90 insurance AR from 40 percent to 15 percent, in turn reducing the days in AR from 49 to 25, which is well within the industry benchmarks. It can also increase the percentage of clean claim rates by more than 99 percent. Additionally, the radiology group could obtain transparency into the root causes of the delay-in-claim submission. With the implementation of technology, it improved the delay in submission from seven to four days.

While each group has its unique challenges, the approach of automation implementation is the same. The first step is to identify the most critical challenges and inefficient processes to address.

Conducting a workflow analysis is crucial to identify where automation can drive efficiencies. Once an analysis is complete, they should re-engineer the key processes and prioritize which workflows to automate based on the ease of implementation and return on investment. Many organizations make the mistake of attempting to automate current inefficient manual processes without re-engineering them or accurately evaluating the workflow.

An automation solution does not replace existing technology investments but rather supplements the shortcomings of core and ancillary systems. Automation for administrative functions offers a layer that helps organizations improve their productivity and focus on more value-added tasks while minimizing time and costs that arise from repetitive tasks. This, in turn, helps accelerate cash flows and improve operating margins.

Teri Gatchel-Schmidt, MBA, CPC, has more than 28 years of experience in the healthcare industry as a practice administrator, operational leader, and revenue cycle optimization consultant to large health systems and physician organizations. Over the past 18 years, she has worked extensively with healthcare systems to enhance their financial performance in academic and physician ambulatory-based settings. Her extensive hands-on knowledge of end-to-end revenue cycle processes gives her the ability to identify workflow redesign and overall performance improvement opportunities with an eye towards financial improvement and increased efficiency.

Automated solutions can improve productivity while minimizing time and cost.

Improve Capturing Revenue within Radiology Groups

Patients who have metastatic kidney cancer could benefit from undergoing baseline brain imaging, according to a recently published study.

Journal of the National Comprehensive Cancer Network

investigators from both Memorial Sloan Kettering (MSK) Cancer Center and Gustave Roussy Institute, a leading academic cancer center in France, showed that incidental brain metastases can occur in a clinically significant percentage of patients who have newly diagnosed metastatic renal cell carcinoma.

Consequently, they said, implementing brain imaging – even in asymptomatic patients – could positively impact overall survival rates.

For their study, they included 1,689 patients with metastatic renal cell carcinoma who had been considered to participate in a clinical trial at either facility between 2001 and 2019. These individuals had no clinical suspicion of brain involvement, but they had undergone brain CT or MRI.

When researchers evaluated the patients' scans, they found 72 patients (4.3 percent of the group) had asymptomatic brain metastases. Of that group, 22 were diagnosed via screening for first-line studies, and 50 via screening in the treatment-refractory setting. For those patients, the median 1-year overall survival rate was 48 percent, and the median overall survival was 10.3 months.

Brain imaging is routinely implemented for kidney cancer patients who show evidence of central nervous system involvement, but that is not the case for patients who are symptomatic, they said. Given these results, it could be useful to conduct brain imaging in this patient population.

“With 4 percent overall incidence in this cohort, one might conclude that baseline brain imaging should be considered in all patients with metastatic kidney cancer, particularly those with multi-organ involvement and/or pulmonary metastases,” said lead MSK researcher Ritesh R. Kotecha, M.D.

The team also identified other areas of cancer spread in patients who had brain involvement. At initial kidney cancer diagnosis, 43 patients (60 percent) had metastatic disease, but by the time of brain metastasis diagnosis, 62 patients (86 percent) had metastatic disease in two or more additional organ systems. The most common was lung (66 patients, 92 percent), followed by liver and bone (18 patients each, 25 percent).

“In current practice, chest, abdomen, and pelvis are routinely imaged from the time that metastatic disease is first detected, yet many oncologists do not image the brain,” said senior researcher Martin H. Voss, M.D., medical oncologist with MSK.

According to Eric Jonasch, M.D., professor of genitourinary medical oncology at The University of Texas MD Anderson Cancer Center, the findings of this study are important for two main reasons.

“First, they show that the overall prognosis of patients with brain metastases is consistently worse than the broader population of patients with metastatic renal cell carcinoma. We need to develop a deeper scientific understanding of why this patient population has a worse outcome, and we need to include them in future clinical trials,” said Jonasch, who is the vice chair of the NCCN Clinical Practice Guidelines in Oncology Panel for Kidney Cancer. “Second, they underscore the utility for MRI imaging of all patients with metastatic renal cell carcinoma both at initial diagnosis, and at regular intervals, to detect occult brain metastases, since specific treatment strategies are required for this patient population.”

The team did note that their study did not elucidate how frequently brain imaging in these patients should be repeated, pointing to the need for further study.

Study finds clinically significant percentage of patients with metastatic renal cell carcinoma develop asymptomatic brain metastases. 

Brain MRI and CT Could Identify Metastasis in Patients with Kidney Cancer

Low-dose CT (LDCT) isn’t just for lung cancer screening. Data gathered from the scans can also help predict a patient’s five-year risk of death from cardiovascular disease.

investigators from The Netherlands detailed how implementing a deep learning algorithm that is fueled with information captured during a low-dose CT exam for lung cancer screening can offer valuable details about calcification in the heart and the aorta.

Using LDCT in this way is a two-for-one opportunity to screen patients for both potentially deadly conditions. The scan is used to detect lung cancer in high-risk individuals, including heavy smokers, but for those individuals, cardiovascular disease actually surpasses lung cancer as the leading cause of death. By picking up these areas of calcification that are linked to plaque build-up, the scan can give providers a strong predictor of heart attacks, stroke, and mortality from cardiovascular disease.

Given the recent recommendation update from the U.S Preventive Services Task Force that opened up LDCT lung cancer screening to high-risk individuals ages 50-to-80 who have at least a 20-pack year smoking history, it is possible that providers will be able to pick up on cardiovascular disease mortality risk in an even wider population of patients, the team said.

In the past, data pulled from CT images has been used in conjunction with other risk factors, such as cholesterol levels, blood pressure, and self-reported history of illness. But, for this study, the team, led by Bob D. de Vos, Ph.D., from Amsterdam University Medical Center and the Image Sciences Institute at the University Medical Center Utrecht, drew upon deep learning to create an automated method that can quickly predict five-year cardiovascular disease mortality without putting a heavier work burden on providers.

“We have shown that five-year cardiovascular disease mortality can be predicted for lung screening participants in less than half a second, using only site-specific calcium scores automatically derived from lung screening low-dose CT,” the team said. “Hence, the proposed image-based analysis could aid in identification of lung screening participants at risk for cardiovascular disease mortality, without relying on self-reported participant data.”

Figure 2: Projections of all aligned chest CT scans show feasibility of slab-based quantification of calcium, resulting in an average image. For alignment, only translation, rotation, and scaling were allowed, resulting in a blurry image, because not all anatomy is exactly the same across participants. From left to right, the center axial, sagittal, and coronal sections are shown. Note that field of view is similar to cardiac CT, which is a consequence of image alignment by the used automatic calcium scoring method. Image alignment allowed the determination of calcification distributions into slabs as a proxy measure for proximal and distal calcifications, of which the borders are indicated by the horizontal lines.

For the study, they pulled data from LDCT scans conducted between August 2002 and April 2004 on 4,451 participants with an average age of 61 who were part of the National Lung Screening Trial. Using those details, the team trained their deep learning method to quantify six types of vascular calcification: thoracic aorta calcification (TAC), aortic and mitral valve calcification, coronary artery calcification (CAC) of the left main, the left anterior descending, and the right coronary artery. After training, they tested their method on data gleaned from 1,113 participants.

The team compared the methods with semi-automatic baseline prediction using self-reported participant characteristics, such as age, smoking history, and history of illness. According to their analysis, the prediction model using calcium scores performed better than the baseline model.

Specifically, the prediction model that used calcium scores reached a C statistic of 0.74 while the baseline model that relied on only participant characteristics achieved 0.69. The model had the best results, they said, when all variables were combined – a 0.76 C statistic.

“The analysis shows we found predictors that are typically not described in a literature, possibly because we performed analysis in lung cancer screening participants we are already at high risk of cardiovascular disease from a history of heaving smoking and the presence of extensive arterial calcification,” de Vos explained.

The method works in two stages, he said, first using deep learning to pinpoint the amount and location of arterial calcification in the coronary arteries and aorta. The second stage relies on a more conventional statistical approach to mortality prediction and identifies features that are most predictive of five-year mortality.

Incorporating the deep learning method into lung cancer screening would be simple, de Vos said, because it requires no special equipment and does not tack on any exam time. And, the benefits to providers and patients could be immense.

“The method uses only image information, it is fully automatic, and it is fast. The method obtains calcium scores in a complete chest CT in less than half a second. This means that the method should be easy to implement to routine patient work ups and screening,” he said. “Lung screening studies show that heavy smokers die from cardiovascular disease as much as from lung cancer. But, we also see that some people with very high calcium scores survive, while others with low scores do suffer from major cardiac events.”

Their results point to a need for additional research that can more specifically point to calcifications that are dangerous. To date, they have developed several automatic scoring methods that can use varied types of data, but they are continuing on with development. Now, de Vos said, they are concentrating on creating a calcium-scoring method that can use low-quality data, such as data affected by cardiac motion, low image resolution, and high noise level, to detect arterial calcification.

“We developed a method, for example, that can detect coronary calcification even when the lesions are below the clinically used threshold,” he said. “This way, we hope to increase the reproducibility of calcium scoring and enable more accurate prediction.”

But, for now, he said, their work has shown that it is possible to achieve real-time mortality prediction with lung screening chest CT by using only data from automatic deep learning-quantified arterial calcium.

A low-dose CT for lung cancer screening can also predict a five-year cardiovascular disease risk of death when using a deep learning algorithm.

Add Predicting Heart Disease Mortality Risk to LDCT Capabilities

Take a look at this CTA -- what's your diagnosis?

Focal narrowing of the descending aorta distal to the great vessels as visualized on this sagittal view of the aorta on CTA.

CTA and MRA can enable clear cross-sectional visualization of the aortic coarctation. One can also look for collateralization of vessels most commonly involving the subclavian artery to internal mammary artery to intercostal arteries and, then, to the aorta, distal to the coarctation. Rib notching can be seen. Treatment can surgical or balloon angioplasty.

What's the diagnosis for this image captured with CTA?

Image IQ Quiz: What's the Diagnosis for this CTA?

59-Year-Old Patient Presents with Abdominal Pain

Small bowel obstruction secondary to adhesions

Large bowel obstruction secondary to malignancy

- Complication of chronic cholecystitis with formation of cholecystoenteric fistula
- Gallstone passage through fistula, which ultimately becomes impacted within the small bowel (most commonly at ileocecal valve).
- Uncommon cause of small bowel obstruction

What is your diagnosis for this 59-year-old patient who presents with abdominal pain?

Image IQ Quiz: 59-Year-Old Patient Presents with Abdominal Pain

When it comes to breast cancer, imaging and preventive efforts go hand-in-hand. This year, through its Honorary Fellow Award, the Society of Breast Imaging recognized a provider who collaborates closely with imaging colleagues to bring the best level of care to women facing or receiving a breast cancer diagnosis.

Therese Bevers, M.D., is professor of clinical cancer prevention and medical director of the Cancer Prevention Center and prevention outreach programs at MD Anderson Cancer Center. In addition to overseeing the first comprehensive clinical cancer prevention service program that opened in 1996, she concentrates her efforts of breast cancer prevention, screening, and diagnosis.

For additional Society of Breast Imaging conference coverage, click 

 spoke with Bevers about what she sees as the main priorities in the fields surrounding breast cancer, as well as the main challenges that exist and what the future brings with imaging.

 How did you become interested in breast cancer prevention and screening? What was in your background or your education that led you to want that to be your focus?

Therese Bevers, M.D.
MD Anderson Cancer Center

 Interestingly, my background and my board certification is in family medicine. Prevention is a big component of primary care, and I eventually went into preventive medicine as a field of practice. Then, I was hired by MD Anderson to run the newly opened Cancer Prevention Center in 1996. That's when I really focused my career on cancer prevention, screening, and diagnosis. The natural area for me was breast cancer prevention and screening and diagnosis. I quickly became involved in the breast cancer prevention trial, which was the trial of tamoxifen versus placebo, that showed that tamoxifen reduced the risk of developing breast cancer by one-half. That just naturally led to expanding my career in the field of breast cancer prevention, screening, and diagnosis. I don't do any treatment – my background is primary care. Although, I have become a specialist in the field of preventive medicine and cancer diagnosis.

 What do you consider to be your most significant contributions to the field surrounding breast cancer?

 I think my biggest contribution has been in a leadership role largely through the National Comprehensive Cancer Network (NCCN), but through other entities, as well. That's my biggest area where I have led the breast cancer screening and diagnosis guideline panel that talks about how we should screen women at average and increased risk, and how we make the right diagnosis. Then, I also chaired the breast cancer risk reduction guideline panel, which talks about how to reduce a woman's risk of developing breast cancer, not only through tamoxifen and Raloxifene and aromatase inhibitors, but also through healthy lifestyle changes and for very high-risk women through prophylactic mastectomy.

I feel that my biggest contribution has been through that arena because I feel that as far as breast cancer risk reduction, I have helped to lead practice-changing guidelines there in the field of breast cancer screening. I, along with SBI and the American College of Radiology, have really endorsed, strongly supported, and being very vocal about the importance of beginning annual mammograms at age 40. The U.S. Preventive Services Task Force really recommends biennial or every other year mammograms beginning at age 50. I think if COVID-19 does nothing else, it shows us the error of going to every other year. Many women have not gotten their screening during COVID-19. So, some of their intervals have stretched out two years, and we are seeing more advanced diagnosis of breast cancer. I feel very strongly that the data supports getting an annual mammogram beginning at age 40.

So, the SBI and ACR are very vocal about that, along with my co-chair of the NCCN panel. Having said that, I would like to note that I think it is the members, many members of SBI, who have really taught me all that I know about screening mammography and the data supporting annual beginning at age 40.

What are the most significant challenges you see facing efforts for breast cancer prevention, screening, and detection?

 For screening, it's that there are a number of different recommendations out there that just simply confuse women. Women don't know what to do, and when you don't know what to do, many people take the action of not doing anything. That concerns me, and I wish that we had a more unified voice for breast cancer screening, unfortunately, we don't.

I am very blessed in the institution that I work in that I get to work literally across the hallway from our breast imagers. I get to have daily one-on-one interactions with them about the symptoms a woman presents with the clinical findings that I identify and what their imaging findings are. We get to approach it collaboratively. Unfortunately, in the real world, out in the community, that doesn't get to happen often. That’s the biggest challenges for many clinicians who are trying to do the best that they can for women in making appropriate recommendations and making an accurate diagnosis. They just don't have that one-on-one collaboration with their breast imager.

What do you see as the main priorities in the field today or 5-to-10 years out?

Looking at new ways of screening for breast cancer, we have seen huge advances. I remember when I started at MD Anderson, there was a huge film library dedicated just to mammograms and breast imaging studies. Now, that film library has gone to the wayside and we have digital images. We now have expanded into tomosynthesis – in street lingo called 3D mammography – where we're actually getting images of the breast in slices like a CT scan would do. One other modality that has come since I started practicing 25 years ago is being able to offer women breast MRI if they're at high risk of developing breast cancer. That wasn't something we offered when I started in the field in 1996. But, now, it's codified in the NCCN and American Cancer Society recommendations for women at increased risk of breast cancer.

We’ve already made great strides that helped us to improve imaging. But, there are so many new modalities out there that are coming down the pipe. Areas I think that we will see continue to grow, at least in the field of imaging, would be contrast enhanced mammography where not only do we get the images, but we get functional information if contrast is injected. Is there a role for molecular breast imaging in screening? Maybe, maybe not. It has a higher dose of radiation. So, that's a bit of a concern. Certainly, it is an opportunity in the diagnosis of breast cancer because it gives functional information, especially for women that can't do MRI whether due to claustrophobia or body habitus. we're seeing more and more imaging modalities.

There's a lot of interest in identifying the Holy Grail of tumor markers in the blood that would identify women at risk for developing breast cancer or women that actually have breast cancer. If we had a tumor marker in the blood or saliva or whatever body fluid that we use, that allows us to move away from mobile mammography vans and brick-and-mortar buildings out to maybe a mall or a health fair where we could draw blood or get the saliva specimen. Then, we could circle back to that woman and say, “Hey, you're okay,” or the next woman and say, “We're a little concerned with what we saw. We need to get you in now for the imaging and do a workup to see if there is actually something going on.”

For you, in your career, what’s on the horizon? What is your next endeavor or area of focus?

I intend to continue with NCCN and leading those two guideline panels. I think there's a lot of opportunity. I have seen our NCCN and breast cancer screening and diagnosis guideline panel grow from a few pages to now close to 30 pages, We encompass not just the straightforward screening of average risk women and the diagnosis of just breast masses and nipple discharge, etc., but to encompassing more rare conditions like anaplastic large cell lymphoma (LCL), breast implant associated LCL, as well as breast cancer in men and breast pain imaging in the pregnant and lactating woman. That’s a big interest for me – being able to take what experts know and what the data has shown and being able to put it in a recommendation or a guideline such that community physicians can easily access it and say, “Ah, I should do that. I didn't think of that.”

MD Anderson Cancer Center medical director shares her thoughts the priorities and challenges facing breast cancer detection and screening.

For SBI Awardee: Face-to-Face Collaborations, a Challenge; New Modalities, Exciting Possibilities

Chugging along through my work list this past week, I was stopped in my tracks by a honking-big liver mass that stymied my brain’s pattern-recognition circuits. I’ve come to think of such abnormalities as WOE lesions (“What on Earth…?”).

A WOE differs from a “Whoa!” lesion, although this particular mass qualified for the latter category, too: Big enough you could see it from across the room, bad, and/or a striking change from prior comparison studies.

The initial challenge with a WOE is knowing for sure that it 

 something you’ve seen before. Okay, you haven’t seen anything like it lately, but there are only so many abnormalities out there, right? So the first thing that happens is a ransacking of the long-term memory for something that might not even be in its cobwebby recesses.

Answers not being found within, external sources take over. It used to be higher on my list of options to ask a relevant-subspecialty teammate what he thought. However, now that I’ve seen 20-plus years of imaging studies, it’s become way too common that a case making me say “What on Earth…?” will prompt the same mystified reaction from my chosen consultant. I’m more likely to waste his time (and my own), possibly embarrassing him, than I am to get a solid answer.

So I start hitting various references, thanking my lucky stars that the Internet has long since supplanted the chore of reaching for my bookshelves and turning pages in search of, again, something that they might not contain. Sometimes, I hit paydirt: The web coughs up an article showcasing my WOE, or at least something resembling it enough to make me feel vaguely competent if I offer it as a diagnostic possibility.

But, again, if something was weird enough to make me say WOE, that’s an uphill battle. Maybe there aren’t any look-alike cases out there. Maybe I didn’t choose the right search terms. Maybe I did, but the answer I sought would’ve been on the eighth page of Google-hits and I gave up on the fifth.

At this point, I might finally be willing to risk wasting a colleague’s time by begging for an assist. There are still times this works; I don’t think I’ll ever reach a point in my career when I won’t consider it. At the very least, it can be satisfying to see that someone wiser than me is equally stymied; ignorance, like misery, appreciates company. It’s also useful to hear a trusted source tell you what they think a WOE almost certainly is 

, so you don’t go ahead and mention wrong possibilities in your eventual report.

So the WOE might not be something that’s previously offended your eyes, nor those of your trusted sources. Rare things do exist…and it’s not impossible that you’re the first person who’s ever seen a particular bit of pathology. Or, as they used to say about some cases from Louisville oral board exams, an “atypical presentation” of a common entity.

For whatever reason, it’s pretty much unheard of for a rad’s report to plainly say, “I don’t know what this is, and haven’t been able to figure it out.” Instead, we get descriptive: The thing is this size, has that density/signal/avidity, enhances (or fails to) in such a way.

I, then, imagine a conversation I’m having with the clinician who referred the WOE. (After the awkward “What is it?” “I dunno.”) The next questions s/he’d ask would probably be along the lines of “Do I need to worry about it?” and “How do we figure out what it is?”

So, I emphasize things like rate of growth, aggressive/destructive features, or the reassuring absence of such things. Ordinarily, I do everything possible to avoid pointing out another rad’s “miss,” but sometimes it’s a necessary evil here. For instance, my liver WOE from this past week was present on a CT from over a year ago…but, the CT’s reader hadn’t noticed it. As it was retrospectively stable, at least I could say that it wasn’t a rapidly-growing cancer or rampant infection.

(I also made sure to point out that it was extremely subtle on that prior exam, which was a CTPA, whereas my current MR rendered such pathology far more conspicuous. I feel like, if you’re going to talk about a colleague’s miss on the record, you should do what you can to explain why the miss wasn’t that bad.)

Then, I consider whether there’s information I’m missing, and “think out loud” about it in my report. If I haven’t been given any relevant prior studies, that doesn’t mean they don’t exist. I’m very free with suggesting that every effort be made to determine whether priors might be retrieved from other facilities, or at least reports of same. If the clinical history I was given seems at all scant, I’ll point out that missing details from it could turn out to be relevant.

I suspect I’m closer to the conservative edge of the rad spectrum when it comes to suggesting further imaging, especially follow-up studies of the same type that are currently failing to provide a diagnosis. Still, sometimes that seems to offer the best chance of getting answers: They didn’t do this scan with contrast, so let’s try including IV next time. The patient was breathing and moving around during this study; let’s do another with special efforts at avoiding that. The lung lesion was kind of buried in dependent atelectasis during this CT; let’s do the next one prone.

One thing I’d urge folks presented with a WOE: Try not to be overwhelmed by the negative aspects of the situation. Yes, it’s a challenge. You might not get the right answer. You might even wind up looking less than your best, when all’s said and done. But really, how frequently do you get to see something new at this stage of your career? Enjoy the tussle of blazing your own trail. You can nestle in your well-trod comfort zone while you do the other 99.9 percent of your job.

Follow Editorial Board member Eric Postal, M.D., on Twitter, 

When something you see on an image stops you in your tracks.

The WOEs of Radiology

In this week’s preview, here are some highlights of what you can expect to see coming soon:

The American Roentgen Ray Society (ARRS) is holding its 2021 Virtual Annual meeting this week. There will be a wide variety of sessions touching on all models, as well as award winners who will be recognized for their contributions to the field. Look for our ongoing coverage of the conference this week.

For more ARRS 2021 Virtual Annual Meeting coverage

Interoperability and information-blocking guidelines set out by the Office of the National Coordinator went into effect earlier this month on April 5. Despite news coverage of these regulations, many in healthcare, including imaging facilities, were not prepared and are still not in compliance. 

spoke with Matthew Michela, chief executive officer of Life Image, about the reasons behind this lag in compliance, what it means for the industry, and what providers can do to fall in line with the regulations. Look for our podcast with him later this week.

For other interviews with and columns by Matthew Michela,

Having HIV can also have cardiovascular implications for infected individuals. Later this week, look for coverage of a study that examines how the virus can impact a patient’s heart health.

Here's what to expect this week on Diagnostic Imaging.

Diagnostic Imaging's A Look Ahead: April 19, 2021, to April 25, 2021

The threshold for risk associated with renal transplant biopsy is much greater when systolic (SBP), diastolic (DBP), or mean arterial (MAP) blood pressure are evaluated together.

In an award-winning poster presented during the ARRS 2021 Virtual Annual Meeting, Winston Wang, MS, from the Mayo Clinic Arizona detailed there is no statistically significant threshold for increased risk based on each of these values alone.

“When these metrics are combined,” he said, ”the risk of complication is significantly higher when the SBP is ≥ 180 mm Hg, DBP is ≥ 95 mm Hg, and MAP is ≥ 116 mm Hg.”

For their study, Wang’s team reviewed consecutive ultrasound-guided renal transplant biopsies conducted between Aug. 1, 2015, and July 31, 2017. They pulled pre-procedure data from each patient for SBP, DBP, and MAP. Developments of a major bleeding complication – denoted as Common Terminology Classification for Adverse Events grade 3 and above – were recorded in the electronic medical record, but there was no indication of a blood pressure threshold to cancel the biopsy, they said.

The team evaluated 1,689 biopsies from 958 patients – 547 men and 411 women -- and they discovered only 10 patients (0.59 percent) had bleeding complications. Their analysis showed there was no statistically significant difference between biopsies with complication and those without complication for SBP (p=0.351), DBP (p=0.088), or MAP (P=0.132).

The team did note that previous research has also indicated a slight correlation between major hemorrhagic complications of renal transplant biopsy and elevated SBP and DBP values when compared to patients who have normal-range blood pressure.

“The data is limited, based on only four complications,” they concluded.

Using systolic, diastolic, and mean arterial blood pressure values together results is a greater threshold for risk.

The WOEs of Radiology

Diagnostic Imaging's A Look Ahead: April 19, 2021, to April 25, 2021

Renal Transplant Biopsy Risk Threshold Greater When Based on Combined BP Values