Tag: medical imaging

MRI for Prostate Cancer Staging

Posted on by Brian Casey

Digital rectal exams for prostate cancer staging could become a thing of the past thanks to MRI. A new study in JAMA Network Open found that MRI performed as well as digital exams in determining the extent of prostate cancer disease.

Prostate cancer screening is moving closer to becoming a more widely accepted test, and MRI has played a major role in that evolution by enabling more precise workup of men with high PSA levels.

  • But what about other aspects of prostate cancer diagnosis and treatment, such as staging men found to have clinically significant disease? It turns out MRI has a role to play there as well. 

To learn more, researchers from Germany looked at data from 4.4k men with a median age of 66 and median PSA level of 7.4 ng/mL (anything over 3 ng/mL is typically referred for additional workup). All patients were scheduled for radical prostatectomy. 

  • Patients received digital rectal exams to assign clinical T stage and assess characteristics of cancer severity, such as local tumor extent, extracapsular extension, and seminal vesicle invasion. 

This was compared to multiparametric MRI scans on 1.5T or 3T systems, with data reported using the PI-RADS scale. The study’s primary outcome was distant metastasis-free survival. 

Among the patient cohort, researchers found…

  • MRI staging was slightly more accurate than digital exams for predicting biochemical recurrence-free survival (C index = 0.62, with 0.5 representing chance-level prediction and 1 indicating perfect prediction). 
  • And MRI was also better at predicting distant metastasis-free survival (C index = 0.67).
  • But MRI and digital exams were comparable when using four of the major prostate cancer risk classification systems in Europe.

What to make of the results? MRI didn’t have a huge advantage over digital rectal exams, but it was good enough for the authors to suggest that digital exams could be eliminated in favor of MRI workup instead.

  • That would enable clinicians to avoid many of the digital exam’s shortcomings, such as subjectivity, operator dependency, and restricted ability to assess extracapsular extension or seminal vesicle invasion.

The Takeaway

This week’s study shows that MRI’s role in prostate cancer diagnosis and treatment goes beyond just screening, and the digital rectal exam’s role for patient staging could soon become a thing of the past in all but a few cases. 

Where Do Humans Belong in the AI Loop?

Posted on by Jack Troy

AI has its skeptics and superfans when it comes to interpreting medical images, inspiring a wide range of approaches to checking its work, but a new RadioGraphics paper argues the right level of oversight lies somewhere in the middle. 

There’s a strong consensus on the importance of monitoring AI post-deployment, even as the right level of rigor remains an open question. 

  • The FDA and many of its global peers require institutions to track AI performance, implement human oversight, and have a corrective action plan. 
  • Past studies show oversight mechanisms are top of mind for many radiologists. 

The international researchers behind the article say a human-on-the-loop (HOTL) model is the sweet spot for radiology departments as they balance safety and reliability with efficiency. 

  • Under this framework, radiologists don’t have to review every AI output.
  • At the same time, the proposed model doesn’t take AI results at face value.
  • HOTL threads the needle by alerting humans to drift and accuracy problems.
  • If performance drops, the work of reading these scans reverts to radiologists. 

The model acknowledges that even the most cleverly designed radiology algorithms can be sensitive to changing inputs.

  • Scanner upgrades, workflow adjustments, and shifts in patient mix can leave AI that aced training tests out of step with clinical realities. 

HOTL has its perks, but the authors note a few challenges. 

  • Excessive alerts risk desensitizing monitoring teams, so it’s important not to set the notification threshold too low or treat minor deviations as urgent. 
  • Declines in subgroup performance can go unnoticed if overall metrics stay stable.

The Takeaway

For radiology departments, the ideal AI oversight plan protects patients without making the technology more trouble than it’s worth. It seems the HOTL system outlined in RadioGraphics checks those boxes by focusing on trends over individual outputs. Even so, the debate over how to best supervise AI is sure to remain lively. 

Top 6 Radiology Trends for the First Half of 2026

Posted on by Brian Casey

The first half of 2026 is now in our rear-view mirror. As we do every year, we’ve compiled a list of the top six stories – one for each month – to help recap what was important in medical imaging.

Radiology Reporting Booms as Microsoft Sunsets PowerScribe 360
Microsoft’s announcement in February that it would be sunsetting its PowerScribe 360 radiology reporting software set off a scramble for market share that continues months later. While PowerScribe was instrumental in moving radiology to speech recognition-based reporting, many radiology facilities are seeing the announcement as a chance to adopt more modern AI-powered reporting solutions.   

Radiology Dominates List of New AI Approvals
The FDA regularly updates its list of AI-enabled medical devices with marketing authorizations, and our coverage of the agency’s decisions was the second most-popular story of 2026’s first half. As with previous updates, radiology dominated the list, garnering 76% of all authorizations since the agency began keeping count and 75% in the fourth quarter of 2025.

Residency Push Skips Radiology
Workforce shortages are a hot story across healthcare, and radiology is no exception. But the specialty won’t be getting much help from a federal initiative to add more resident training slots. Of the more than 400 residency programs awarded funding so far, only two diagnostic radiology programs were selected.

Radiologist Quit Rates Double in a Decade
Having to do more work with less personnel could be convincing many radiologists to leave the profession. Our readers paid close attention to a February story on a JACR study that documented a doubling of the radiologist quit rate over 10 years, and the exact point in terms of case workload when rads were most likely to leave. 

Data Is Lacking on AI’s ROI
As radiology AI slowly moves from pilot projects to widespread clinical adoption, a new survey reveals a paradox: The technology is popular with radiologists, but few imaging facilities using AI have collected hard data showing its return on investment. That’s according to another popular story from April. 

Study Finds Variation in Radiologist Workload
Our sixth and final top story of 2026 addressed the growth in imaging volume since the COVID-19 pandemic, and how radiologists responded. Researchers found that volume did indeed grow faster than the supply of radiologists, but some imagers were doing more than others in picking up the slack. 

The Takeaway 

Our readers have apparently been interested in workload issues so far this year, as evidenced by the fact that three of the top six stories on The Imaging Wire for the first half of 2026 had something to do with radiology’s rising exam volume and its ramifications. The other half broadly addressed AI and imaging IT issues. See a connection?

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.

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