Diagnostic Imaging brings you daily coverage of RSNA 2021 live from Chicago. 

RSNA 2021 - Live Coverage From Chicago

Radiological Society of North America (RSNA) 2021 Annual Meeting

 found that racial ethnic minorities, and patients with low socioeconomic status, lower access to broadband internet or limited English proficiency were less likely to use a patient-portal self-scheduling pathway for screening mammograms.

“Understanding the factors associated with patient use of technology and patient portals for access to care can guide interventions to bridge the existing digital divide and promote health equity in radiology,” the authors wrote.

The results were presented by Patricia Balthazar, M.D., assistant professor of abdominal radiology and imaging informatics at Emory University in Atlanta, Georgia.

While patient portals can increase patient access and engagement, they can create potential barriers to care. In this study, the researchers aimed to identify potential sociodemographic disparities in the use of an online patient portal to self-schedule screening mammograms compared with traditional scheduling pathway, such as a scheduler phone call and referral system. The researchers hypothesized that patients with limited English proficiency or low socioeconomic status, along with racial/ethnic minorities would be less likely to use the online self-scheduling pathway.

This retrospective cohort study included 46,268 female patients undergoing screening mammograms from Jan. 1, 2019, to Dec. 31, 2019, at an urban quaternary-care academic medical center. The following data were extracted: patient scheduling pathway, age, language, race, health insurance provider and zip code, with the latter linked to Census data to extract the following demographic information: internet access, median household income and education level.

Of the patients, 0.7% used the online self-scheduling pathway. Those using the self-scheduling pathway had higher odds of being younger (OR for age in year: 0.94; 95% CI: 0.93-0.96), English-speakers (95% CI: 21.6, 3.0-156.5), White (95% CI: 1.7, 1.2-2.5), have private insurance (95% CI: 1.5, 1.0-2.1) and to live in zip code areas with higher percentage of access to broadband internet (95% CI: 1.2, 1.1-1.3).

Unlike racial ethnic minorities, patients with low socioeconomic status, lower access to broadband internet and limited English proficiency, “patients living in zip code areas with higher median household income, education level or with any type of internet access were not significantly associated with the online self-scheduling pathway,” the authors wrote.

Articles

This study discloses the characteristics of patients using self-scheduled online patient portal screening mammography. 

Online Patient Portals for Screening Mammograms May Widen Health Disparities 

68Ga prostate specific membrane antigen (PSMA) positron emission tomography (PET) has high utility in the detection of suspicious lesions in biochemically recurrent prostate cancer. This is according to study results presented at the 

“PSMA PET is a more robust modality for biochemical recurrence with the potential of identifying small lesions which would otherwise be dismissed on MRI alone,” the authors wrote. “We recommend the use of 68Ga PSMA PET/MR for biochemical recurrent prostate cancer with potential to be the new standard of care for this indication.”

The results were presented by Juana Martinez, M.D., fellow in radiology at Weill Cornell Medical College in New York. 

PSMA PET combined with magnetic resonance imaging (MRI) is a promising technique in the evaluation of patients with prostate cancer. In this study, the researchers compared PSMA PET with concomitant multiparametric MRI in detecting lesions suspicious for biochemical prostate cancer recurrence. 

A review was performed of all patients with prostate cancer who prospectively enrolled in a clinical trial of 68Ga PSMA PET/MRI at New York Presbyterian Hospital - Weill Cornell campus from April 2018 to January 2021. Nuclear medicine physicians read the PSMA PET portion of the study and a radiologist with fellowship training in cancer imaging read the MRI of the skull base to thigh, independent of each other. Patient demographics, the indication for PSMA PET/MRI and serum prostate specific antigen (PSA) levels closest to the PSMA PET/MRI were extracted from patient charts.

Of the 165 patients who had a PSMA PET/MRI, 109 where imaged for biochemical recurrence. PSMA PET was able to detect more lesions suspicious for recurrence than MRI at all PSA levels. When assessing only patients who had PSMA PET/MRI for biochemical recurrent disease, PSMA PET fared superiorly at all PSA levels in comparison with MRI. 

“When compared with MRI, PSMA PET is highly robust in identifying lesions which are otherwise deemed unremarkable,” the authors wrote.

PSMA PET is highly robust in identifying prostate cancer lesions which are otherwise deemed unremarkable. 

Prostate-Specific Membrane Antigen PET Bests MRI in Biochemical Recurrent Disease 

The concurrent use of an artificial intelligence (AI) tool with digital breast tomosynthesis could improve the diagnostic performance of radiologists in the detection of breast cancer. This is according to a study presented at the 

 that also found that the addition of AI improved the efficiency of radiologists’ workflow.

“This AI system can be integrated into screening practice to improve both efficiency and accuracy of digital breast tomosynthesis,” the authors wrote.

The results were presented by Pierre Fillard, Ph.D., the founder and chief scientific officer of Therapixel SA in Nice, France.

The retrospective study aimed to determine the benefits that an AI tool could bring to the time and accuracy of digital breast tomosynthesis interpretation. A total of 22 breast specialist radiologists were asked to interpret 240 digital breast tomosynthesis examinations with and without the help of AI. The dataset included 114 biopsy-proven cancers, of which 36 were classified as false negative. Performances were measured in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity and reading time.

The average AUC across readers was 0.79 without AI and 0.83 with AI, for an average improvement of 0.04 (95% CI: 0.02 to 0.06, 

 < .001). Average sensitivity improved from 80% without AI to 82% when using AI (2%; 95% CI: -0.4% to 0.4%), while specificity increased from 56% to 61% (5%; 95% CI: 1.5% to 8.7%). Average reading time was 74.7 seconds without AI and 70.9 seconds with AI (average difference: -3.8; 95% CI: -8.4 to 0.8). The reading time reduction varied in accordance with the likelihood of malignancy assigned by the AI, ranging from -15.6% for cases assigned with a low likelihood of malignancy to -0.5% for the most suspicious cases.

When applying these results to a screening population, the observed reading time was on average 76.2 seconds without the help of AI, which the authors noted “lies in the lower bound of the reading time reported for real-word practice.”

“Nonetheless, an average reduction of 8% was observed in assisted readers, which indicates that a regular batch reading (i.e., a sequential interpretation of screening mammograms without interruption) could include 13% more cases (about seven cases per hour) thus improving the efficiency of the screening program and reducing the time-to-diagnosis for patients,” the authors wrote.

An artificial intelligence for digital breast tomosynthesis enhances radiologists’ performance and efficiency. 

AI for Digital Breast Tomosynthesis Enhances Diagnostic Performance 

My last few columns were aimed at radiologists whose careers resembled my own: Satisfying/successful in other regards, but not achieving the traditional “goal” of equity partnership or something similar. I presented systems that let me parallel such a path, bypassing compensation-ceilings which would have constrained me in one rad group by switching to others whose ceilings were more porous, or at least higher.

Readers who’ve achieved partnership for themselves, or expect to in the future, might not see much in those columns for their own use. Instead, I can imagine some reacting pretty negatively if their worldview is that one should patiently and obediently climb the ladder of a single workplace…remaining loyal to it even if a trapdoor gets slammed shut above the climber.

Such readers might see my attitude as unhelpful to their own systems. What if a bunch of rads adopted my approach, and groups’ non-partner employees were inspired to leave for better gigs? What if the partners started having to pay out more to recruit and retain the rads necessary to keep their operations running?

Upper-level rads able to see from other viewpoints, however, and put themselves in the shoes of their lower-tier colleagues, would be able to see that those columns hold just as much value for them as anyone else. The other side of the coin is right there, for those able/willing to turn it over.

If you’re a partner, or otherwise in group-leadership, you stand a better chance of getting and keeping rads by maintaining an awareness of what they want. Coupled with a good sense of whether you’re giving it to them…or if you’re not, and other groups are.

Reexamine the reasons why you can’t, or simply don’t want to, give all the rad-members of your team a realistic path to partnership, or something almost as-good-as. If you can make them feel like your group is their career’s “home,” they’ll be much more likely to stay for the long haul rather than if they view it as an apartment from which to trade up.

Put another way: Equity partners generally don’t leave one practice to start over elsewhere, even though some groups’ partners do substantially better for themselves than others. But non-partner rads switch jobs all the time. Part of the difference is a psychological barrier to moving when you’ve achieved equal citizenship in your group.

If that’s simply not something a group’s going to offer, and it tries to retain rads on a permanent “associate” basis, everything held back from them by the owners/partners is going to weigh against this effort. Like Grand Moff Tarkin’s fist: The tighter the grasp on comp, the more good rads will slip away between those metaphorical fingers.

Some rad groups bake that into their business-model, trying little if at all to retain good people. “Churn and burn” works on the premise that replacements can always be hired, so no need to pay your rads a penny more than you absolutely have to at the moment. Maybe they’re desperate to live where your group is, maybe they’ve got checkered backgrounds that leave them few other options, or maybe the job market just isn’t in their favor. Whatever the case, this is 

 the formula for leading a high-quality, cohesive team. It’s more like being a radiological slumlord.

That’s not to say that a no-partnership, permanent-employee model is doomed to mediocrity or failure. If you start a radiology group and offer daytime telerad contracts at $400/hr (and can somehow make the finances work such that you don’t go bankrupt), I’m pretty sure you’ll be able to recruit and retain a pretty darned capable team. As mentioned above, if you offer terms that are “as-good-as” partnership to a given rad, you’ve probably got him for the long haul.

Yes, the dollars matter, but all sorts of other things factor in. Work-schedule, a seat at the table where the group makes its decisions (even a nonvoting chair beats being kept out of the room entirely)...you might be surprised what some folks value. Which is why it’s so important to ask after—not just a one-and-done query, but periodically revisited.

And that’s the other side of my system’s coin, for those recruiting and wanting to retain rads: If you can’t or won’t give them a traditional partner-like status, be attentive to what they consider as-good-as when they’re in the four phases I previously described.

No need to rehash them all here; it’s easy enough to mentally walk through the phases and think about where in that scheme any given employee/contractor is. You can then tailor your approach to them. Do everything you can to keep them in phases 1 and 2, most importantly maintaining open lines of communication. Misunderstandings, disappointments, and resentments between the group and its rads should all be nipped in the bud; if things get to phase 3 (let alone 4!) they get increasingly difficult to fix.

Retaining good people should never be about giving them as little as you can get away with for as long as you can: That’s a system for too-little, too-late action…or for folks you’d just as soon push out the door rather than wait for them to walk through it themselves.

Retaining good people should never be about giving them as little as you can get away with for as long as you can.

Goals, Systems, and Self-Awarded Raises in Radiology: The Other Side of the Coin

 suggest that magnetic resonance imaging (MRI) alone is a successful method for the follow-up in women who undergo breast-conserving treatment and ensures early detection of recurrent cancer.

“Women in the follow-up after breast cancer who have access to breast MRI may safely forgo additional mammographic or digital breast tomosynthesis follow-up,” the authors wrote.

The results were presented by Vanessa Raaff, Ph.D., of the department of diagnostic and interventional radiology, RWTH Aachen University Hospital in Germany.

For follow-up after breast conserving treatment, serial two-view mammography is recommended, possibly accompanied by targeted ultrasound. As breast MRI has been shown to be useful to improve breast cancer detection of recurrence in patients after breast conserving treatment, the researchers investigated the usefulness of MRI as a standalone imaging method for this purpose.

Between January 2011 and March 2021, a total of 426 women who had undergone MRI alone for routine follow-up after breast conserving surgery were included. The women started MRI screening at a median of two years after treatment and underwent MRI screening annually. Validation of the last negative MRI study was achieved by tomosynthesis plus clinical examination in 137 women and/or by clinical follow-up for at least two years in 289 women.

The women were followed by 1,553 MRI studies, covering a total 1,727 women years. Across the entire follow-up period, MRI was positive in 108/1,553 examinations (6.9%) and in 85/426 women (19.9%). Recurrent breast cancer was confirmed in 64/108 MRI studies and in 55/85 women, for a positive predictive value of 59.3% per positive MRI study and 64.7% per patient. 

The cancer-detection-rate was 37 per 1000 women years. Recurrent cancers were detected on average 8.9 years after breast conserving treatment. Of the 64 recurrent cancers, 17.2% were ductal carcinoma in situ and 82.8% were invasive. The mean size of invasive recurrent cancer was 11.4 mm, most frequently staged as T1b, followed by T1c.

“None of the women received a diagnosis of breast cancer in between MRI screening rounds, or within the two-year follow up after the respective last negative MRI, or upon digital breast tomosynthesis after the last study MRI,” the authors wrote.

Following breast cancer treatment, women who have access to breast MRI may safely forgo additional mammographic or digital breast tomosynthesis follow-up. 

MRI Alone Beneficial for Surveillance After Breast-Conserving Treatment

 describes a deep learning model that was developed to successfully detect intracranial hemorrhage in computed tomography (CT) scans without medical annotation.

“This method is faster because it removes the need for medical annotation, which can be added later on image errors to improve the model,” the authors wrote. “By detecting 96.6% of the normal exams, we can speed up doctor’s work, providing more time for the analysis of the slices with bleeding, increasing the precision of the diagnosis.”

Intracranial hemorrhage is a life-threatening condition that is diagnosed via CT scans. Deep learning algorithms developed to aid in the detection of bleeding within the intracranial cavity require a physician to annotate exams per slice. In this work, the researchers annotated a dataset of head CT exams using natural language processing (NLP) and applied an architecture using convolutional and recurrent neural networks to classify each exam. 

A total of 8432 head CT exams collected from six hospitals and five specialized outpatient clinics were assembled and the axial volumetric unenhanced series of scans were selected. The labels were extracted from the written reports using a previously trained NLP model. Almost 40% of exams had an intracranial hemorrhage, which were not separated by bleeding subtype. Each exam was processed using brain, blood and bone windows, resliced to 25 images. A MobileNet network pre-trained with ImageNet weights was combined with a Long short-term memory (LSTM) with categorical cross-entropy as loss function. The model’s output was as follows: hemorrhage, no finding and other pathologies. 

The model improved the classification without the need for a per slice annotation. On the validation set, an area under the curve (AUC) of 0.96, 0.87 for recall and 0.97 for precision was achieved. While on an external set, validated by a doctor, the AUC was 0.91, 0.85 and 0.86, respectively. 

The researchers also highlighted slices where the hemorrhage appears to aid diagnosis by applying Gradient Class Activation Map (GradCAM) technique. 

“Through the GradCAM method, we selected the best slices of each exam, and doctors were able to evaluate the results and indicate with more precision where a hemorrhage was found,” the authors wrote. “The identification of the most relevant slices, using the GradCAM technique, alongside clinical information, may help doctors better understand each patient case, accelerating and improving the diagnosis.”

A deep learning model was developed to detect intracranial hemorrhage in computed tomography scans without medical annotation.

Algorithm Can Detect Intracranial Hemorrhage in CT Without Medical Annotation 

In patients with esophageal squamous cell carcinoma (SCC), multiparametric magnetic resonance (MR) features provide useful diagnostic information of pathologic complete response after neoadjuvant therapy. This is according to a study presented at the 

“Multiparametic MR features provide useful diagnostic information of pathologic complete response among patients with esophageal SCC who undergo neoadjuvant therapy,” the authors wrote.

The results were presented by Jinrong Qu, M.D., of the department of radiology, affiliated cancer hospital of Zhengzhou University and Henan Cancer Hospital in Zhengzhou, China.

 earlier this year found that MRI has high sensitivity but low specificity for the detection of esophageal SCC and adenocarcinoma, showing promise for determining neoadjuvant therapy response. Meanwhile, a 

 of 54 patients with locally advanced esophageal SCC found that integrating parameters of diffusion-weighted MR imaging (DWI) with other imaging methods can better identify pathological response of primary tumor to neoadjuvant chemoradiotherapy.

The latest study presented by Dr. Qu included 36 patients with esophageal SCC at stage cT2-T4a/N0-N3/M0 who were retrospectively reviewed. MRI was performed before and after neoadjuvant therapy, followed by surgical resection. The lesions were analyzed for the following five MR features: changes of size, signal on DWI, apparent diffusion coefficient (ADC) map, T2-weighted turbo spin echo (TSE) BLADE and degree of enhancement. Response was assessed by using tumor regression grade (TRG).

The five MR features after neoadjuvant therapy showed strong positive correlations with TRG 0 (r = 0.7432; 

<.001). The 10 cases with TRG 0 showed either complete disappearance of the mass or decreased mass with minimal residual thickening of the esophageal wall. These TRG 0 cases also showed iso- or decreased-intensity on DWI, iso- or increased-intensity on ADC map, iso- or decreased-intensity on T2-weighted TSE BLADE, or homogeneous enhancement on high-resolution delayed StarVIBE. The five MR features were different from all the TRG 2-3 in 20 cases. 

ADC map had the highest performance in distinguishing TRG 0 from non-TRG 0 with the sensitivity of 96.1%, specificity of 76.5%, negative predictive value of 74.7% and positive predictive value of 91.6%

“MR assessment of changes in tumor size, signal on DWI, ADC map, T2-weighted TSE BLADE, and enhancement pattern may distinguish between biopsy proven TRG 0 and non-TRG 0 in patients with esophageal SCC,” the authors wrote.

Multiparametric MRI can detect pathologic complete response after neoadjuvant therapy in patients with esophageal squamous cell carcinoma.

MRI Features Help Detect Neoadjuvant Therapy Response in Esophageal SCC

The quantitative assessment of the deltoid muscle by ultrasound could accurately detect type 2 diabetes, according to a study presented at the 

“Following further studies, this process could translate into a dedicated, simple and noninvasive screening method to detect type 2 diabetes,” the authors wrote. The process could help identify “some of the 232 million undiagnosed persons globally and could prove especially beneficial in screening of underserved and underrepresented communities.”

The findings were presented by Steven Bishoy Soliman, D.O., RMSK, a musculoskeletal radiologist at Henry Ford Hospital & Health System in Michigan.

 by Dr. Soliman that included 137 patients found that the ultrasound appearance of the deltoid muscle in patients with diabetes differed from that in patients with obesity but without diabetes. In the latest study the researchers aimed to evaluate if the sonographic quantitative assessment of the deltoid muscle can be used to detect type 2 diabetes.

In the study, deltoid muscle ultrasound images from 124 patients were independently reviewed by three musculoskeletal radiologists. Patients were categorized as the following: obese type 2 diabetes, non-obese type 2 diabetes, obese non-type 2 diabetes, and non-obese non-type 2 diabetes, with 31 images in each category. The radiologists, who were blinded to the patient’s category, measured the grayscale pixel intensity of the deltoid muscle and humeral cortex to calculate a muscle/bone ratio for each patient. Over three weeks, the radiologists repeated measurements on a random 40 patients. Patient age, gender, race, body mass index, insulin usage and hemoglobin A1c level were analyzed and the difference among the four groups was compared. Inter- and intra-radiologist variability or agreement were also assessed

The results showed a statistically significant difference in muscle/bone ratios between the groups with the average ratios as follows: obese type 2 diabetes, 0.54 (

 < .001); non-obese type 2 diabetes, 0.48 (

 < .001); obese non- type 2 diabetes, 0.42 (

 = .03); and non-obese non- type 2 diabetes, 0.35.

Further, excellent inter-observer agreement (ICC 0.87) and intra-observer agreements (ICC 0.92, 0.95, and 0.94) were found. The sensitivity for detecting type 2 diabetes was 80% with a specificity of 63%.

“Quantitative deltoid muscle ultrasound can detect type 2 diabetes with the potential for a highly sensitive noninvasive screening method,” the authors wrote.

Sonographic quantitative assessment of the deltoid muscle could translate into a dedicated, simple and noninvasive screening method to detect type 2 diabetes.

Deltoid Muscle Ultrasound May Detect Type 2 Diabetes 

Patients with suspected axial spondyloarthropathy (AxSpA) not taking non-steroidal anti-inflammatory drugs (NSAIDs) are more likely to have MRI proven inflammation compared with those taking NSAIDs, according to study results presented at the 

“Undiagnosed AxSpA can have significant impact on patients’ physical and mental health but also their quality of life and livelihood,” the authors wrote. “It is possible that NSAID use at the time of scanning reduces the diagnostic yield of MRI scanning.”

The findings were presented by Prasanna Partha Sarathy, MBChB, BMedSc, of Singleton Hospital in Swansea, UK.

Early AxSpA diagnosis, which depends on HLA-B27 testing and MRI imaging, is essential. While NSAIDs can lead to improvement in MRI appearances, patients suspected of having AxSpA are often prescribed these drugs for symptomatic relief before imaging. In this study, the researchers assessed the use of MRI in patients with suspected AxSpA and the effect of NSAIDs on the diagnostic yield of MRI scanning.

The six-month, multicenter, retrospective cohort study included 74 patients referred for MRI of the spine for suspected AxSpA. At the time of MRI, the patients were asked about NSAID use. The MRI results were assessed for evidence of active inflammation. 

MRI showed signs of AxSpA in 22% of the patients. Those not using NSAIDs at the time of the scan were more likely to have MRI proven inflammation (

 = .017). After adjusting for age and gender, the study found that patients not taking NSAIDs were more likely to have MRI proven inflammation compared with those taking NSAIDs (OR 8.3; 95% CI, 1.9-36; 

= .005). This finding was mainly driven by the no NSAID versus regular NSAID use subgroups (OR 14; 95% CI, 2.1-97; 

 = .006). No difference was seen between patients not taking NSAIDs and those taking NSAIDs as required, or between patients taking NSAIDs as required versus those taking regular NSAIDs.

“This study suggests suspected AxSpA patients not taking NSAIDs were more likely to have MRI proven inflammation compared to patients taking NSAIDs especially those on regular NSAIDs,” the authors wrote.

They suggested that more prospective research is needed to explore this relationship further.

Findings suggest that patients with suspected axial spondyloarthropathy not taking NSAIDs were more likely to have MRI proven inflammation compared with patients taking NSAIDs.  

NSAID Use May Affect Axial Spondyloarthritis Diagnosis via MRI

 demonstrates the importance of using racially diverse datasets while training artificial intelligence (AI) systems to ensure fair outcomes.

“As the rapid development of deep learning in medicine continues, there are concerns of potential bias when interpreting radiological images,” the authors wrote. “As future medical AI systems are approved by regulators, it is crucial that model performance on different racial/ethnic groups is shared to ensure that safe and fair systems are being implemented.”

The findings were presented by Brandon Price, a medical student at Florida State University College of Medicine in Tallahassee.

Many studies have shown that deep learning systems are subjective in their interpretation of data. Bias is often accidentally introduced into the training data, or a racial or ethnic group is under sampled causing susceptible models to develop bias. In this study, the researchers investigated how a deep learning model trained on a dataset lacking racial diversity could impede the detection of pathology in underrepresented minority patients.

The researchers used a dataset with over 300,000 chest X-ray images and 14 labeled findings. A low sample size of other races/ethnicities meant that only images of Black and White patients were included. One training dataset included only White patients and the other training dataset comprised 26% Black and 74% White patients. An equal distribution of labeled findings was shared between the datasets and a DenseNet model was trained on each dataset 25 times. The receiver operating characteristics (ROC) area under the curve (AUC) and sensitivity, with a specificity threshold of 0.75, were compared for each of the 14 labeled findings.

Compared with a model trained on only White patients, the model trained with a diverse dataset had a significantly better ROC-AUC performance at identifying six of the 14 labeled findings in a test dataset of only Black patients (

 <0.05). Additionally, compared with a model trained on only White patients, the model trained with a diverse dataset found a significant increase in sensitivity performance for six of the 14 labeled findings on a test dataset of only Black patients (

“As more AI systems are developed, it is imperative that they are fair and perform equally well with groups that have been historically underserved,” the authors wrote.

Deep learning models trained on a dataset lacking racial diversity could hinder the detection of pathology in underrepresented minority patients. 