Tag: radiology

Mammo Modality Face-Off for Early Breast Cancer

Posted on by Brian Casey

When it comes to early breast cancer detection, which medical imaging modality is best: full-field digital mammography, digital breast tomosynthesis, or breast MRI? A new study in Clinical Radiology picks winners – and brings the receipts. 

Breast imagers are fortunate to have many technologies at their disposal, each with its own strengths and weaknesses. 

  • X-ray-based mammography tools like FFDM and DBT are easily available and relatively low cost, while breast MRI delivers the highest resolution but is expensive, less available, and more time-intensive to perform. 

So when does it make sense to use each modality? Researchers from China tested four techniques – FFDM, DBT, and breast MRI at 1.5T with accelerated and full protocols – in 329 patients with early-stage breast cancer (maximum tumor diameter ≤ 2 cm). 

  • They also analyzed results according to breast tissue density, as dense breast tissue is not only a cancer risk factor but can also obscure lesions on X-ray-based modalities.

Across the study sample, researchers found…

  • There was little difference in sensitivity between the four techniques for women with non-dense breast tissue, with FFDM, DBT, and accelerated breast MRI achieving 91% compared to 94% for full-protocol breast MRI.
  • But breast MRI pulled ahead in sensitivity for women with dense breast tissue, both with accelerated and full protocols (95% and 94%) beating DBT and FFDM (90% and 83%).
  • Accelerated breast MRI had performance comparable to the full protocol regardless of breast density, but at almost half the median scan time (8 vs. 15 minutes).
  • Accelerated and full-protocol breast MRI had the same specificity (94%), ahead of both DBT and FFDM (88% and 83%).

What to make of the results? Researchers said the findings in women with non-dense breast tissue reinforce that X-ray-based modalities are sufficient.

  • For women with dense breast tissue, accelerated breast MRI offers performance close enough to the full protocol that breast imaging practices can feel comfortable offering the faster exam.

The Takeaway

It’s no surprise that breast MRI beat both FFDM and DBT mammography for early breast cancer detection in women with dense breast tissue. But it is intriguing that there wasn’t much difference between breast MRI with either accelerated or full protocols. That’s good news for practices that want to make this powerful modality accessible to more women. 

Interventional Radiology’s Practice Evolution

Posted on by Brian Casey

Interventional radiology has proven benefits for patient care, enabling life-saving procedures to be performed less invasively than open surgery. But interventional radiology procedures are being concentrated among fewer radiologists, based on findings from a new study in JVIR by researchers from the ACR’s Neiman HPI group. 

From its origins in pioneering work conducted in the 1960s by Charles Dotter, MD, in image-guided minimally invasive procedures, interventional radiology has evolved into a field with one foot in diagnostic radiology and another in therapy.

  • The field achieved a major milestone in 2012, when it was recognized as an independent, primary medical specialty, and shortly thereafter an integrated IR/DR pathway was adopted that gives trainees additional dedicated interventional training. 
  • This replaced the previous practice of just tacking an extra interventional fellowship on to a diagnostic radiology program.

Has the new training structure changed who’s performing interventional procedures in the U.S.? Neiman HPI researchers examined this issue by analyzing Medicare claims from 2008 to 2023 for 46k radiologists. 

  • They focused on the volume of interventional procedures being performed by radiologists, and any shifts in volume that could have resulted from changes in the training program.

Over the study period, researchers found…

  • The percentage of all radiologists who performed at least some interventional work fell (from 67% to 50%).
  • But the percentage of super-specialists – those who spent more than 90% of their time doing interventional work – more than doubled (from 4.1% to 8.8%).
  • Among radiologists who primarily performed interventional work, more were younger compared to older (25% vs. 12%).
  • And super-specialists tended to be younger as well (9.2% vs. 6.8%). 

The changes are most likely due to the new IR/DR training pathway. But they also raise new questions, such as whether interventional radiology should completely separate from diagnostic radiology in both training and practice settings.  

  • The authors weren’t ready to go that far, noting that the integrated IR/DR pathway was designed to ensure dual competency in both image interpretation and procedures, and such flexibility is still valuable in today’s healthcare environment. 

The Takeaway

The new findings on the concentration of interventional radiology practice generally reflect the trend toward increased specialization that’s being seen in both radiology and healthcare. Patients are benefiting, as their procedures are more likely to be performed by specialists who not only received more training but also have more experience than in the past.

AI Risk Prediction’s Long-Term Value

Posted on by Brian Casey

AI-based calculations of breast cancer risk derived from screening mammograms can track cancer risk as it evolves over time, giving clinicians a longitudinal tool for following patients who might need additional care. A new study in Radiology adds to the growing body of knowledge on AI-based risk analysis. 

Cancer risk prediction has emerged as a promising new application for AI, as exemplified by a study earlier this month in which three commercial AI models for screening mammograms were also able to predict risk as much as six years before diagnosis. 

  • At least one AI model – Clairity Breast from Clairity – has received FDA clearance for image-based risk prediction, with others under review at the agency. 

But most studies of AI-powered breast cancer risk prediction calculate risk at a single point in time. 

  • While that’s useful, a woman’s breast cancer risk can evolve with factors such as breast tissue density, which is known to change over time – thus changing their risk profile. 

So authors of the current study tracked breast cancer risk longitudinally using the Mirai algorithm, an open-source model that’s been validated in previous studies as more accurate than clinical risk prediction models like Tyrer-Cusick and BCRAT.  

  • They retrospectively applied Mirai to 54k women who got mammograms from 2009 to 2019, and compared changes in risk scores between women who developed cancer and those who didn’t. 

Researchers found… 

  • Median risk scores six years before diagnosis changed from 2.1 to 6.6 in women eventually diagnosed with cancer.
  • Risk scores were essentially stable in women who were cancer-free (1.8 to 2.2).
  • Risk scores rose at a higher annual rate longitudinally in those with cancer versus those without (1.13 vs. 0.09 per year).
  • Women in the group who developed cancer tended to be older and had dense breast tissue or a personal or family history of breast cancer. 

Exactly what is the AI detecting if cancer isn’t visible to radiologists reading the mammograms?

  • Most likely, AI is detecting changes in patterns of breast parenchymal tissue that “may precede radiographic detection.” These changes are basically biomarkers that can be used to develop personalized screening intervals, supplemental modalities, and other preventive strategies. 

The Takeaway

The new study on AI-based breast cancer risk prediction advances our understanding of how risk can be calculated far in advance of a cancer diagnosis. It’s easy to see this knowledge put to use with earlier intervention strategies that exemplify the rise of personalized medicine. 

Midjourney’s Imaging Spa Concept Raises Eyebrows

Posted on by Brian Casey

In one of the more novel business concepts we’ve seen in a while, generative AI algorithm developer Midjourney last week announced a new wellness project that combines whole-body imaging with a spa atmosphere. The company hopes to have the first imaging spa – outfitted with what it calls an “ultrasonic CT scanner” – up and running in 2027.

If you’re not familiar with Midjourney, you’re not alone. The San Francisco company launched in 2022 with one of the first generative AI algorithms able to create images from text prompts. 

  • Midjourney’s business model is rooted in industries like graphic design and advertising, where its algorithm is employed to create artistic prototypes of concepts that can then be forwarded to human artists for finalization.

Fast forward to last week’s announcement. Acknowledging that the idea was “a little weird and a little crazy,” the company said its Midjourney Medical business would combine the healthcare and spa industries in a concept that could radically expand the approach to whole-body screening and longevity imaging.

  • It starts with a technology that’s not necessarily new to medical imaging – ultrasound tomography in a water bath (water is an excellent conductor of sound waves). But Midjourney Medical turns the water bath into a gigantic, warm pool into which customers will be lowered on a gently descending elevator.

On the way down, spa-goers will pass through an array of half a million tiny ultrasound transducers that produce “terabytes of data each second” that are then sent to powerful computers for processing with AI.

  • Midjourney claims AI will be able to recognize changes in density or stiffness and will create a 3D map of the body “that looks a lot like today’s MRIs but at nearly a hundred times the speed.” 

But wait, there’s more. Stating that it wants to create an experience that isn’t just about health “but is just a nice place to go,” Midjourney said the scanners will be built around spas with hot tubs, saunas, cold plunges, and “cozy rooms with pools of golden light.” 

  • The first Midjourney spa is scheduled to open in San Francisco toward the end of 2027, with additional locations planned in 2028.

The Takeaway

The radiology world is littered with the remains of startup companies that thought they could reinvent the discipline with new technologies radically different from modalities that are already available. Is Midjourney Medical one of them? Or will the firm’s ambitious concept of an imaging spa revolutionize whole-body screening? Only time will tell. 

Top 10 AI Vendors by FDA Approvals

Posted on by Brian Casey

Who are the top 10 radiology AI vendors, based on the number of FDA regulatory authorizations? The agency provided some clarity this week with an update to its list of authorized AI-enabled medical devices through the end of Q1 2026. 

The FDA updates the list on more or less a quarterly basis, and it’s become a closely watched barometer for tracking not only the health of the AI industry but also which companies have received the greatest number of authorizations.

  • As we’ve noted in the past, the list includes both standalone AI algorithms as well as medical hardware that has AI functionality embedded in it, like a mobile X-ray machine with an onboard AI feature for detecting fractures.

The updated list tracks marketing authorizations through the end of March 2026, and shows that the FDA has…

  • Authorized 1,524 AI-enabled medical devices since it began keeping track in 1995, up 5.1% from Q4 2025. 
  • Authorized a total of 1,164 radiology devices, or 76% of all AI-enabled medical authorizations. 
  • In the first quarter of 2026, the FDA authorized 92 AI-enabled medical devices, or 28% more than in the fourth quarter of 2025.
  • For the quarter, 69 authorizations (75%) were for radiology devices, about the same ratio as in Q4 2025 (76%). 
  • GE HealthCare held its lead as the company with the most radiology AI authorizations at 130 (including recent acquisitions that had AI authorizations of their own).
  • Next is Siemens Healthineers at 95, then Philips at 58, Canon at 48, United Imaging at 40, Aidoc at 33, and DeepHealth at 29, with all numbers including acquisitions. 
  • Rounding out the top 10 are Samsung (21), Rapid.ai (20), and Hyperfine (13).

The Takeaway

The FDA’s new numbers on AI marketing authorizations show that the agency is keeping pace with rapid developments in the healthcare AI industry. Indeed, the FDA is even accelerating its pace of product approvals compared to its last update, with radiology still securing the lion’s share of authorizations.

SIIM 2026 Video Highlights

Posted on by Brian Casey

The annual meeting of the Society for Imaging Informatics in Medicine is always one of the highlights on the radiology calendar. SIIM 2026 was no exception, once again underscoring the vibrant community driving advances in imaging IT.

From radiology reporting to enterprise image management, SIIM 2026 highlighted the state of the art in imaging IT. We talked to many of radiology IT’s key opinion leaders in Pittsburgh, and we’re pleased to bring the discussions to you in this newsletter.

We hope you enjoy watching our SIIM 2026 video coverage as much as we enjoyed producing it! 

Check out the SIIM 2026 video links below or visit the Shows page on our website, and keep an eye out for our next Imaging Wire newsletter on Thursday.

– Brian Casey, Managing Editor

Top Trends from SIIM 2026

Posted on by Brian Casey

Last week’s SIIM 2026 conference demonstrated once again radiology’s ongoing evolution, from a discipline once known for big iron to one dominated by software. From radiology reporting to the evolving AI platform segment, below are the top seven trends from Pittsburgh. 

  • Reporting Stays Red Hot: Radiology reporting was the top theme from SIIM 2025, and the segment got even hotter with Microsoft’s decision to sunset its PowerScribe 360 radiology reporting software, which has drawn a host of new competitors into the segment. At SIIM 2026, a common theme was enterprise imaging companies adding reporting modules to their solutions.  
  • AI Adoption Moving Slowly But Surely: Adoption of radiology AI has been frustratingly slow, but it’s moving inexorably toward broader clinical use. At SIIM 2026, some 68% of the radiology-oriented papers focused on AI in some way, especially the new generation of foundation and vision language models that are enabling targeted AI algorithms to be developed more quickly than ever.
  • AI Governance Gets Real: Growing adoption of AI algorithms is creating a new issue: How to manage all this new technology. AI governance therefore was a major issue at SIIM 2026 as healthcare providers debated the legal and ethical necessity to better manage AI adoption, deployment, and utilization.
  • Other ‘Ologies Get into the Act: Radiology likes to think of SIIM as its own conference, but it also encompasses other ‘ologies that are moving into digital image management, like pathology and ophthalmology. At SIIM 2026, several imaging IT vendors showed integration with data from these disciplines, giving healthcare institutions a single source for their healthcare data management.
  • The Rise of All-in-One Vendors: A growing number of imaging IT vendors are rolling out solutions that combine image viewer, worklist, and reporting into a single platform, simplifying purchasing, deployment, and maintenance for radiology customers. Many of these firms seem to be getting traction with potential buyers, indicating the all-in-one concept could be one whose time has come.
  • Agentic AI Takes Shape: Agentic AI is a growing trend in radiology as algorithm developers build solutions to take on mundane tasks and free up radiologists to focus on their primary task: interpreting images. But the question is, will agentic AI work in the real world, or simply pile more technology on clinicians?
  • What Next for AI Platforms? Bayer’s withdrawal from the AI platform market by pulling its support for Blackford in 2025 raised many questions about the platform model that persisted at SIIM 2026. AI platforms seem to be evolving to add additional services like AI monitoring and governance.

The Takeaway

SIIM may not be radiology’s largest show, but for those in the imaging IT space it may be the most valuable one outside of RSNA. SIIM 2026 proved that point, with the top trends from Pittsburgh illustrating the discipline’s direction at the midpoint of the radiology year. For our overview of the top trends at SIIM 2026, check out our YouTube channel or the Shows tab on our webpage.

AI for Breast Cancer Risk

Posted on by Jake Fishman

Artificial intelligence may be capable of identifying subtle mammographic signs of breast cancer years before conventional diagnosis, according to a new study published in Radiology. Researchers from Sweden found that three commercially available AI algorithms for mammography screening generated elevated cancer scores as early as 10 years before diagnosis, with detection signals strengthening as diagnosis approached.

Predicting breast cancer risk offers the prospect not only of detecting cancer earlier, but also of tailoring mammography screening to women most likely to benefit from it.

  • Clinical risk calculators like Tyrer-Cuzick and breast density analysis are available, but AI-based algorithms are showing promise by predicting risk from screening mammograms.

In the new study, researchers analyzed 89k mammograms from 31.4k women collected over a 10-year period, drawn from Sweden’s national screening program, where women aged 40-74 undergo biennial mammography interpreted by two radiologists.  

  • During the study period, 12.1k women (39%) were ultimately diagnosed with breast cancer. Three commercially available AI algorithms were used to generate risk scores (Vara AI from Vara, Lunit Insight MMG from Lunit, and MammoScreen from Therapixel). (It’s worth noting all three were originally designed for cancer detection rather than risk prediction.) 

AI scores increased progressively over time in women who later developed cancer, while remaining relatively stable among cancer-free participants…

  • At 90% specificity, AI systems flagged 19%-20% of future breast cancer cases six years before diagnosis.
  • Detection increased to 23%-25% at four years before diagnosis.
  • Performance rose further to 35%-39% at two years before diagnosis.
  • Even 10 years before diagnosis, the systems identified 13%-17% of future cancers.
  • Across all pre-diagnostic examinations, AI achieved AUC values of 0.63-0.67, outperforming mammographic density alone (AUC = 0.57).

The findings suggest that AI tools developed for cancer detection may also have value as early-alert systems for identifying women who could benefit from closer surveillance or supplemental imaging.

  • While prospective validation is still needed, sequential AI scoring may ultimately help identify women who would benefit from supplemental imaging, closer surveillance, or earlier intervention.

The Takeaway

The study adds to growing evidence that mammography AI can extend beyond cancer detection to long-term risk stratification. By identifying subtle imaging patterns years before diagnosis, AI-derived detection scores could provide an additional layer of longitudinal risk monitoring and help guide more personalized screening strategies.

Radiology’s ‘Zombie Jobs’ Go Unfilled

Posted on by Brian Casey

Is radiology’s workforce shortage really just a matter of “geographic inconvenience?” A new report suggests that job shortages are mostly isolated to areas that radiologists consider to be less desirable geographically, where “zombie jobs” go unfilled for months.

The workforce shortage in radiology (and healthcare for that matter) has become a common refrain, especially since the COVID-19 pandemic. 

  • Exam volumes are rising steadily with an aging population, but the radiology workforce remains static. 

At least, that’s how the story goes. But a new study from RadBoard.io challenges that narrative, claiming that many radiologist openings are going unfilled because they are in geographically undesirable areas. 

  • RadBoard’s Kirill Lopatin analyzed 20.8k job postings in the U.S. over 78 days that represented 11k unique job ads (at least 47% of ads were just re-posts of the same role, but the number is probably much higher). 

The study found that of the postings with complete “lifespans” (from initial posting to deactivation)…

  • 25% were filled in less than one week, and another 12% in 7-14 days.
  • 28% took 31-60 days to fill.
  • 2.3% took 61-90 days (and maybe even longer).

So nearly one-third of radiology job ads were open longer than a month, leading RadBoard to conclude that radiology didn’t have a single fill rate for open positions – “it has two markets layered on top of each other.” 

  • RadBoard called radiologist job ads open for more than 60 days “zombie jobs,” with some markets having higher “stuck rates” as calculated by ads open longer than 60 days divided by total open job ads. 

States with the worst job markets by stuck rate included Nebraska (68%), Minnesota (41%), and Washington state (35%). 

  • At the other end of the spectrum were Florida (18%), Texas (15%), and New York (14%). 

This led RadBoard to conclude that the radiologist shortage was not a national problem – it was concentrated in areas where radiologists didn’t want to live.

  • Also, jobs in stuck markets paid $175k less than those in faster-cycling markets ($550k vs. $725k) – the opposite of what might be expected in a scarce market.

The Takeaway

The new numbers offer an eye-opening look at the narrative around the radiologist shortage, indicating that it may be more nuanced than previously thought. And the subtext to the data hints at the divide in U.S. healthcare between rural areas and metropolitan regions.

SNMMI 2026 News Highlights Theranostics Growth

Posted on by Brian Casey

The growing importance of theranostics was on display at this year’s annual meeting of the Society of Nuclear Medicine and Molecular Imaging in Los Angeles. New data on theranostics agents in development dominated the scientific sessions, while on the diagnostics side a proliferation of new PET radiotracers promises to go beyond FDG. 

The selection of SNMMI’s Image of the Year went to South Korean researchers for their work on the radiotracer 18F-GP1 PET/CT to identify acute lower extremity deep vein thrombosis.

  • In a study with 46 symptomatic patients, the tracer showed high diagnostic accuracy for detecting clots not only in the thigh but also in the calf, and had a high detection rate of pulmonary embolism occurring together with DVT. 

Meanwhile, the Abstract of the Year award was given to a study using PET to link brain metabolism patterns to the effectiveness of treatments for Alzheimer’s disease.

  • UCLA researchers performed FDG-PET brain scans on 124 patients being considered for anti-amyloid therapy. Those whose scans suggested Alzheimer’s disease and who got therapy had higher cognitive scores at one year compared to patients whose PET scans didn’t show evidence of Alzheimer’s. 

Other SNMMI 2026 highlights included…

  • FDG-PET/CT scans showed that patients who got bariatric surgery had metabolic changes across multiple organs that correlated with improved clinical markers. 
  • A new PET tracer, gallium-68 RCC78, was able to detect clear cell renal cell carcinoma and identified additional metastatic lesions missed by standard imaging.
  • A first-in-human study with a novel PET radiotracer, carbon-11 nevanimibe, was presented for imaging patients with overactive adrenal glands. 
  • In patients with metastatic neuroendocrine tumors, a new type of peptide receptor radionuclide therapy with actinium-225 DOTA-LM3 showed promise.
  • PSMA-PET scans showed that prostate cancer patients with just one to five bone metastases had much worse outcomes than patients with no metastases. 
  • A novel approach with two PET radiotracers during cancer treatment detected both tumor progression and cardiac inflammatory response. 
  • A novel PET tracer, fluorine-18 OXD-2314, showed promise for detecting chronic traumatic encephalopathy in living patients.
  • An AI algorithm using data from pre-therapy PET/CT scans predicted radiation dose in lutetium-177 PSMA treatment for prostate cancer.

The Takeaway

This year’s SNMMI 2026 highlighted the exciting evolution of theranostics, from a niche treatment used mostly when other therapies failed to a major step on the road to personalized medicine – and better patient care.

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