Tag: radiology

AI Recon Cuts CT Radiation Dose

Posted on by Brian Casey

Artificial intelligence got its start in radiology as a tool to help medical image interpretation, but much of AI’s recent progress is in data reconstruction: improving images before radiologists even get to see them. Two new studies underscore the potential of AI-based reconstruction to reduce CT radiation dose while preserving image quality. 

Radiology vendors and clinicians have been remarkably successful in reducing CT radiation dose over the past two decades, but there’s always room for improvement. 

  • In addition to adjusting CT scanning protocols like tube voltage and current, data reconstruction protocols have been introduced to take images acquired at lower radiation levels and “boost” them to look like full-dose images. 

The arrival of AI and other deep learning-based technologies has turbocharged these efforts. 

They compared DLIR operating at high strength to GE’s older ASiR-V protocol in CCTA scans with lower tube voltage (80 kVp), finding that deep learning reconstruction led to …

  • 42% reduction in radiation dose (2.36 mSv vs. 4.07)
  • 13% reduction in contrast dose (50 mL vs. 58 mL).
  • Better signal- and contrast-to-noise ratios.
  • Higher image quality ratings.

In the second study, researchers from China including two employees of United Imaging Healthcare used a deep learning reconstruction algorithm to test ultralow-dose CT scans for coronary artery calcium scoring. 

  • They wanted to see if CAC scoring could be performed with lower tube voltage and current (80 kVp/20 mAs) and how the protocol compared to existing low-dose scans.

In tests with 156 patients, they found the ultralow-dose protocol produced …

  • Lower radiation dose (0.09 vs. 0.49 mSv).
  • No difference in CAC scoring or risk categorization. 
  • Higher contrast-to-noise ratio.

The Takeaway

AI-based data reconstruction gives radiologists the best of both worlds: lower radiation dose with better-quality images. These two new studies illustrate AI’s potential for lowering CT dose to previously unheard-of levels, with major benefits for patients.

U.K.’s Massive Diagnostic IT Project

Posted on by Brian Casey

The U.K. is planning a massive project – worth close to $1B – to procure new IT tools for medical diagnostic use. While details of the plan are still sketchy, it involves the acquisition of both radiology and cardiology PACS, as well as AI.

The U.K.’s NHS has become one of the world’s hottest test beds for medical IT adoption as the service struggles to reconcile a static workforce with rising demand for healthcare services.

  • For example, the NHS last year issued the AI Diagnostic Fund, which provided £21 million ($28M) for a variety of AI implementation projects across 64 NHS trusts.

But the new tender offer dwarfs that investment. NHS has proposed a Digital Diagnostic Solutions project to serve as “a route to market for departmental wide diagnostic IT solutions.”

  • The value of the project is pegged at £700M ($923M), a massive investment in medical IT by any metric. 

The offer is being led by NHS Supply Chain, the governmental agency responsible for procuring medical equipment within the NHS. 

  • The program’s tender offer states that the Digital Diagnostic Solutions project “is to be the new Framework for the Medical IT Departmental Software and Hardware Solutions framework within NHS Supply Chain.”

It includes the following provisions: 

  • Acquisition of radiology PACS, cardiology PACS, RIS, cardiovascular information systems (CVIS), laboratory information management systems (LIMS), and vendor-neutral archives (VNAs).
  • Software acquired through the program “will sit alongside” other capital equipment like X-ray, MRI, and CT systems.
  • It will also include 3D software, diagnostic AI software, and endoscopy image management applications.

Publication of an invitation to tender will happen in December 2024, and the contract award will be in July 2025, with the framework itself starting in August 2025. 

The tender offer was published just a few days before a government-commissioned report that said the NHS was in “serious trouble” and that was harshly critical of the system’s transformation to digital operation.

  • And that report came after a July election that saw the Labour party win power for the first time in 14 years – raising hopes that it would approach NHS funding differently than the previous Conservative governments. 

The Takeaway

Does the Digital Diagnostic Solutions project represent a new commitment to funding IT innovation from the Labour government? Or is it simply a rebranding of the NHS’ existing procurement activities? Stay tuned. 

Combo CT Screening Detects More Disease

Posted on by Brian Casey

A CT lung cancer screening program that also offered abdominal non-contrast CT scans detected a large number of abnormalities outside the lung in a population of people with smoking histories. The combined approach could offer a more efficient way to detect multiple pathologies in a single patient visit. 

CT lung cancer screening is gaining momentum globally, but clinicians and researchers continue to look for ways to make it more valuable. 

  • That’s a good thing, because smoking is a risk factor not just for lung cancer, but also other pathologies like abdominal aortic aneurysm (AAA) – so why not screen for those at the same time?

In a paper in European Urology, U.K. researchers describe their Yorkshire Kidney Screening Trial (YKST), which sought to detect kidney cancer by piggybacking on the county’s existing CT lung cancer screening program. 

  • Abdominal non-contrast CT exams were offered at the same time as thoracic CT lung screening scans to high-risk people who met the lung program’s screening criteria, namely aged 50-85 and more than 30 pack-years of smoking history. 

In all, 4k people accepted the offer to get additional abdominal CT scans, which had the following findings …

  • 64% of patients had normal findings, while another 20% had images that required additional review but no further action.
  • 5.3% had a new serious finding.
  • Serious findings were broken down as follows: renal stones ≥ 5 mm (3%), AAA (1.5%), renal mass/complex cysts (0.62%), kidney cancers (0.25%), and other cancers (0.25%).
  • It took 13 minutes of additional time to perform the abdominal CT scan.

Researchers said the prevalence of additional disease in YKST was within the range of other U.K. screening programs, such as for colorectal cancer (0.16-0.61%) and breast cancer (0.92%). 

  • The high prevalence of AAA was “unexpected,” especially since many AAA cases were found in people who aren’t covered by existing AAA screening programs. 

The Takeaway

As with recent research combining CT lung screening with coronary artery calcium (CAC) scoring, the new study shows that lung screening offers an opportunity to screen for more than just lung cancer. By detecting additional disease, combo screening has the potential to flip the script when it comes to screening’s cost-benefit ratio. 

Why the FDA’s Density Rule Matters

Posted on by Brian Casey

The FDA’s new rules on reporting breast density to women getting mammograms went into effect on September 10. The implementation has been expected for some time, but this week’s rollout generated a wave of positive press coverage that highlights the importance both of breast density awareness and of breast screening.

The FDA in March 2023 said it would implement a national standard requiring providers to inform women of their breast density, which can obscure lesions on conventional X-ray mammography. 

  • Breast density is also a risk factor for cancer, and patient advocacy groups had been pressuring the FDA to set a standard to replace what has become a patchwork of state-by-state notification rules. 

The FDA’s rules have been incorporated into the Mammography Quality Standards Act, and require that … 

  • Mammography reports include a plain-language patient summary with “an overall assessment of breast density.” 
  • The summary must include specific language that defines breast density, explains its ramifications for detection and cancer risk, and suggests the need for additional imaging tests.

A novel aspect of the new rules is that they were mostly driven by patients – women like JoAnn Pushkin and the late Nancy Cappello who as patients discovered first-hand the shortcomings of X-ray-based mammography for women with dense breast tissue. 

What’s next? Density-awareness proponents are now turning their attention to reimbursement, which for supplemental imaging is inconsistent across the U.S.

  • A fix for the problem – the Find It Early Act – is working its way through Congress, and women’s health advocates lobbied on Capitol Hill this week to try to push the legislation through before the end of the current Congressional session. 

The new reporting landscape also creates opportunities for better software tools to detect and manage breast density and better predict risk in patients with dense breast tissue. 

  • Clinicians already realize that women with dense breasts not only need different screening modalities like MRI and ultrasound, but that they might also require more frequent screening due to their heightened cancer risk. 

The Takeaway

The FDA’s new breast density rules matter for a variety of reasons, from showing the power of patients to change their imaging experience to outlining a future in which risk plays a more prominent role in breast screening. While more work remains to be done, this is a good time to savor the triumph.

Imaging News from ESC 2024

Posted on by Brian Casey

The European Society of Cardiology annual meeting concluded on September 2 in London, with around 32k clinicians from 171 countries attending some 4.4k presentations. Organizers reported that attendance finally rebounded to pre-COVID numbers. 

While much of ESC 2024 focused on treatments for cardiovascular disease, diagnosis with medical imaging still played a prominent role. 

  • Cardiac CT dominated many ESC sessions, and AI showed it is nearly as hot in cardiology as it is in radiology. 

Major imaging-related ESC presentations included…

  • A track on cardiac CT that underscored CT’s prognostic value:
    • Myocardial revascularization patients who got FFR-CT had lower hazard ratios for MACE and all-cause mortality (HR=0.73 and 0.48).
    • Incidental coronary artery anomalies appeared on 1.45% of CCTA scans for patients with suspected coronary artery disease.
  • AI flexed its muscles in a machine learning track:
    • AI of low-dose CT scans had an AUC of 0.95 for predicting pulmonary congestion, a sign of acute heart failure. 
    • Echocardiography AI identified HFpEF with higher AUC than clinical models (0.75 vs. 0.69).
    • AI of transthoracic echo detected hypertrophic cardiomyopathy with AUC=0.85.

Another ESC hot topic was CT for calculating coronary artery calcium (CAC) scores, a possible predictor of heart disease. Sessions found … 

  • AI-generated volumetry of cardiac chambers based on CAC scans better predicted cardiovascular events than Agatston scores over 15 years of follow-up in an analysis of 5.8k patients from the MESA study. 
  • AI-CAC with CT was comparable to cardiac MRI read by humans for predicting atrial fibrillation (0.802 vs. 0.798) and stroke (0.762 vs. 0.751) over 15 years, which could give an edge to AI-CAC given its automated nature.
  • An AI algorithm enabled opportunistic screening of CAC quantification from non-gated chest CT scans of 631 patients, finding high CAC scores in 13%. Many got statins, while 22 got additional imaging and 2 intervention.
  • AI-generated CAC scores were also highlighted in a Polish study, detecting CAC on contrast CT at a rate comparable to humans on non-contrast CT (77% vs. 79%), possibly eliminating the need for additional non-contrast CT.  

The Takeaway

This week’s ESC 2024 sessions demonstrate the vital role of imaging in diagnosing and treating cardiovascular disease. While radiologists may not control the patients, they can always apply knowledge of advances in other disciplines to their work.

AI Detects Interval Cancer on Mammograms

Posted on by Brian Casey

In yet another demonstration of AI’s potential to improve mammography screening, a new study in Radiology shows that Lunit’s Insight MMG algorithm detected nearly a quarter of interval cancers missed by radiologists on regular breast screening exams. 

Breast screening is one of healthcare’s most challenging cancer screening exams, and for decades has been under attack by skeptics who question its life-saving benefit relative to “harms” like false-positive biopsies.  

  • But AI has the potential to change the cost-benefit equation by detecting a higher percentage of early-stage cancers and improving breast cancer survival rates. 

Indeed, 2024 has been a watershed year for mammography AI. 

U.K. researchers used Insight MMG (also used in the BreastScreen Norway trial) to analyze 2.1k screening mammograms, of which 25% were interval cancers (cancers occurring between screening rounds) and the rest normal. 

  • The AI algorithm generates risk scores from 0-100, with higher scores indicating likelihood of malignancy, and this study was set at a 96% specificity threshold, equivalent to the average 4% recall rate in the U.K. national breast screening program.

In analyzing the results, researchers found … 

  • AI flagged 24% of the interval cancers and correctly localized 77%.
  • AI localized a higher proportion of node-positive than node-negative cancers (24% vs. 16%).
  • Invasive tumors had higher median risk scores than noninvasive (62 vs. 33), with median scores of 26 for normal mammograms.

Researchers also tested AI at a lower specificity threshold of 90%. 

  • AI detected more interval cancers at this level, but in real-world practice this would bump up recall rates.  

It’s also worth noting that Insight MMG is designed for the analysis of 2D digital mammography, which is more common in Europe than DBT. 

  • For the U.S., Lunit is emphasizing its recently cleared Insight DBT algorithm, which may perform differently.  

The Takeaway

As with the MASAI and BreastScreen Norway results, the new study points to an exciting role for AI in making mammography screening more accurate with less drain on radiologist resources. But as with those studies, the new results must be interpreted against Europe’s double-reading paradigm, which differs from the single-reading protocol used in the U.S. 

When Follow-Up Falls Short for Lung Nodules

Posted on by Brian Casey

Making sure suspicious imaging findings are followed up appropriately is a key element in providing quality patient care. But a new study found that some suspicious findings aren’t being adequately tracked, especially when it comes to lung nodules. 

Lung nodules are commonly detected on chest CT exams, and are often found incidentally, when patients are being examined for other reasons. 

  • While most smaller nodules don’t represent a threat to patients, it’s important to work up the ones that could be clinically significant. 

In the new paper, Japanese researchers studied 10.5k initial chest CT reports at their institution from 2020 to 2023. 

  • They developed a natural language processing algorithm that analyzed free-text reports to see which ones recommended follow-up. 

They determined that 1.5k reports (14%) recommended additional imaging with exams like chest CT or PET/CT; they then calculated whether these follow-up exams were conducted within 400 days of the initial exam. Further analysis indicated … 

  • For 36% of exams (543) researchers could not confirm that follow-up imaging had taken place.
  • In a random sample of 42 of these patients, 40.5% (17) were not followed up appropriately. 
  • For these cases, either no imaging was documented or no reason was given for the lack of follow-up.

The researchers clarified that they found no evidence of false negatives (missed cancers), as that wasn’t a goal of their study. 

The Takeaway

The new findings indicate both the challenge and opportunity of follow-up management. While radiology must do better in tracking patients with suspicious findings, the study shows that software-based solutions could help, especially those that are automated to scan radiology reports and alert radiologists to cases that need their attention.

Patients Unclear on Imaging Costs

Posted on by Brian Casey

A new study in Health Policy and Technology shows that patients are surprisingly unclear on how much their imaging exams will cost them. Researchers found that few knew their imaging facilities had price estimator tools and even fewer were aware of their out-of-pocket estimates.

The U.S. government has been trying to make healthcare more transparent and understandable for patients through a variety of new rules it’s implemented in recent years, such as “information blocking” rules that prevent providers from withholding patient data.

  • In 2021, CMS required health systems to notify patients of out-of-pocket expenses and make available tools for estimating prices. 

But how knowledgeable are patients about these initiatives? 

  • Researchers from UC Irvine and the University of Michigan surveyed 423 patients scheduled for CT, PET/CT, or MRI scans in Southern California to find out how much they knew about their out-of-pocket costs. 

Researchers discovered that …

  • Only 11% of patients were aware of their out-of-pocket estimates before getting their scans.
  • Only 17% knew their imaging facilities had price estimator tools.
  • 53% said their illness has been a financial hardship, but only 34% were worried about their out-of-pocket costs for imaging.
  • No patient used the hospital’s estimator tool.
  • Patients were less likely to know their out-of-pocket costs if they had lower income (<$50,000), more financial hardship, and no comorbidities. 

The results show that, two years after out-of-pocket transparency rules went into effect, patients are still unclear on their imaging costs. 

  • This is a major problem due to the high variation in imaging prices that’s been documented in other studies, such as 2023 research that found MRI scans ranging in price from $878 to $3,403.

More outreach could help patients better understand costs. 

  • Such outreach could be made through automated calls or even messages through patient portals prior to their exams.

The Takeaway
The new study – when coupled with recent research on patient reports – shows that radiology still has a ways to go when it comes to keeping patients informed about their imaging exams. Getting patients more involved not only will have economic benefits, but could also help patients participate in their own care.

Should Patients Get Their Radiology Reports?

Posted on by Brian Casey

It’s one of radiology’s great dilemmas – should patients get their own radiology reports? A new review article in JACR examines this question in more detail, documenting shifting attitudes toward data sharing among radiologists, referring physicians, and patients themselves.

In reality, the question of whether patients should get their own reports has been settled by the 2022 implementation of federal information blocking rules that prevent providers from withholding patient data. 

  • But open questions remain, such as the best mechanisms for delivering data to patients and how to ensure they aren’t confused or alarmed by radiology findings.

To that end, researchers conducted a systematic review of studies from 2007 to 2023 on patient access to radiology reports, eventually identifying 33 publications that revealed …

  • 70% of studies found patients expressing positive preference toward accessing their radiology reports, a trend consistent over the entire study period.
  • 42% of studies documented patient difficulties in understanding medical terminology.
  • 33% highlighted concerns about patient anxiety and emotional impact.
  • Physician opinions on report sharing shifted from 2010 to 2022, from initial dissatisfaction to a gradual appreciation of its benefits.
  • Most studies focused on patient opinions rather than those of referring physicians and radiologists, whose opinions were found in only 18% and 9% of studies, respectively.

A major problem identified by the researchers is that radiology reports have medical terminology that isn’t easily understood by patients – this can lead to confusion and anxiety.

  • Communicating findings in plain language could be one solution, but the researchers said little progress has been made due to “resistance from radiologists and entrenched reporting practices.” 

Although it wasn’t mentioned by the study authors, generative AI offers one possible solution by using natural language processing algorithms to create patient-friendly versions of clinical reports.

The Takeaway

Once patients get access to their own reports, it’s impossible to put that genie back in the bottle. Rather than debating whether patients should get radiology reports, the question now should be how radiologists can ensure their reports will be understood without confusion by their ultimate customer – patients.

Two-for-One CT Screening Hits the Road

Posted on by Brian Casey

A new study takes CT screening on the road in rural Appalachia, showing how a mobile van outfitted with a CT scanner can screen at-risk individuals for both lung cancer and cardiovascular disease in one visit. 

Recent studies have demonstrated the effectiveness of CT lung cancer screening not only among the overall population, but particularly among disadvantaged communities with lower healthcare access. 

  • Such limited access is common in rural areas of Appalachia, which also have some of the highest rates of smoking and cardiovascular disease in the U.S.

Researchers from West Virginia University wanted to tackle two challenges at once with LUCAS, a mobile van outfitted with a CT scanner for lung cancer screening. 

  • They noted that CT lung scans can also be used to acquire data on coronary artery calcium (CAC), a known risk factor for cardiovascular disease. 

LUCAS was launched in September 2021, so WVU researchers analyzed data acquired for 526 low-dose CT screenings of high-risk people conducted through December 2022. 

  • They used the CT lung scans to calculate CAC scores based on Agatson criteria, in which a score of 101-400 indicates moderate risk of cardiovascular disease and >400 is classified as high risk; individuals with scores ≥100 should be referred to aspirin or statin therapy. 

They found that LUCAS scans revealed … 

  • Over 54% of patients had coronary calcification on LDCT scans
  • 31% of patients had CAC scores ≥100 
  • 14% had CAC scores ≥400
  • Elevated CAC scores correlated with lung cancer risk based on Lung-RADS scores as well as smoking history based on pack-years
  • Of the patients with CAC scores ≥1 and who weren’t already on statin or aspirin therapy, 6.2% started statins and 3.3% started aspirin

Despite the firm link between CAC scores and lung cancer risk, the researchers expressed disappointment that so few patients started prevention therapy like statins or aspirin after their exams.

  • Indeed, researchers noted that few patients from the study got additional cardiac testing or follow-up referrals for cardiovascular prevention after their screenings. 

The Takeaway

The new study not only confirms recent research showing that opportunistic screening can enhance the value of CT lung cancer scans, but also the role that lung exams can play in reducing healthcare disparities. On the down side, it shows that all the screening in the world won’t make a difference if patients don’t get appropriate follow-up. 

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