Tag: radiologists

Did Malpractice Risk Kill V/Q Exams?

Posted on by Brian Casey

CT perfusion angiography exams have largely replaced nuclear medicine-based ventilation/perfusion (V/Q) studies for detecting pulmonary embolism. But a new article in Academic Radiology suggests that CT’s rise wasn’t entirely based on clinical efficacy – fears of malpractice risk may have played a role. 

V/Q studies can help diagnose PE by enabling clinicians to visualize lung perfusion, showing defects such as blockages in pulmonary vessels. The scans are typically performed in three phases … 

  1. An albumin injection to show pulmonary vasculature.
  2. A radiopharmaceutical that’s inhaled and imaged with a gamma camera.
  3. A chest radiograph to correlate findings. 

The scans dominated PE imaging in the 1980s, but the rise of CT saw radiology facilities begin to shift.

  • CTPA was seen as having higher spatial resolution and was easier to perform than nuclear medicine exams. 

But the new article suggests that there were other forces at work as well – in particular, fear of malpractice risk from PEs that weren’t adequately followed after inconclusive V/Q exams.

  • The problem originated with clinical guidelines for V/Q reporting that classified some 20% of V/Q studies as “low probability” for PE when they probably would have better been classified as “inconclusive” or “non-diagnostic.”

As a result, a number of “low probability” patients weren’t followed up adequately, with tragic results that later figured into medical malpractice cases …

  • A patient who was diagnosed with pneumonia after an inconclusive V/Q exam, sent home, and died one day later of a “massive” PE.
  • A patient with leg and chest pain who was given heparin after a negative V/Q scan and later suffered internal hemorrhage; fortunately she survived.
  • A patient with “vague symptoms” who had an inconclusive V/Q scan and later died of an undiagnosed PE that some claimed would have been detected on CTPA.

Indeed, the theme of PE malpractice cases began to shift over time, from failure to diagnose V/Q scans to failure to order CTPA exams – which were soon seen as the clinical gold standard.

The Takeaway

Given the fast pace of development in radiology, it’s inevitable that some technologies that were once clinical staples fall by the wayside. But the new article offers a fascinating look at how clinical language can lead to medico-legal concerns that influence physician behavior – often in ways that are impossible to detect as they happen.

Low-Dose CT Confounds CAD in Kids

Posted on by Brian Casey

When it comes to pediatric CT scans, clinicians should make every effort to reduce dose as much as possible. But a new study in AJR indicates that lower CT radiation dose can affect the performance of software tools like computer-aided detection. 

Initiatives like the Image Wisely and Image Gently projects have succeeded in raising awareness of radiation dose and have helped radiologists find ways to reduce it.

But every little bit counts in pediatric dose reduction, especially given that one CT exam can raise the risk of developing cancer by 0.35%. 

  • Imaging tools like AI and CAD could help, but there have been few studies examining the performance of pulmonary CAD software developed for adults in analyzing scans of children.

To address that gap, researchers including radiologists from Cincinnati Children’s Hospital Medical Center investigated the performance of two open-source CAD algorithms trained on adults for detecting lung nodules in 73 patients with a mean age of 14.7 years. 

  • The algorithms included FlyerScan, a CAD developed by the authors, and MONAI, an open-source project for deep learning in medical imaging. 

Scans were acquired at standard-dose (mean effective dose=1.77 mSv) and low-dose (mean effective dose=0.32 mSv) levels, with the results showing that both algorithms turned in lower performance at lower radiation dose for nodules 3-30 mm … 

  • FlyerScan saw its sensitivity decline (77% vs. 67%) and detected fewer 3mm lung nodules (33 vs. 24).
  • MONAI also saw lower sensitivity (68% vs. 62%) and detected fewer 3mm lung nodules (16 vs. 13).
  • Reduced sensitivity was more pronounced for nodules less than 5 mm.

The findings should be taken with a grain of salt, as the open-source algorithms were not originally trained on pediatric data.

  • But the results do underscore the challenge in developing image analysis software optimized for pediatric applications.

The Takeaway

With respect to low radiation dose and high AI accuracy in CT scans of kids, radiologists may not be able to have their cake and eat it too – yet. More work will be needed before AI solutions developed for adults can be used in children.

Mammography AI Predicts Cancer Before It’s Detected

Posted on by Brian Casey

A new study highlights the predictive power of AI for mammography screening – before cancers are even detected. Researchers in a study JAMA Network Open found that risk scores generated by Lunit’s Insight MMG algorithm predicted which women would develop breast cancer – years before radiologists found it on mammograms. 

Mammography image analysis has always been one of the most promising use cases for AI – even dating back to the days of computer-aided detection in the early 2000s. 

  • Most mammography AI developers have focused on helping radiologists identify suspicious lesions on mammograms, or triage low-risk studies so they don’t require extra review.

But a funny thing has happened during clinical use of these algorithms – radiologists found that AI-generated risk scores appeared to predict future breast cancers before they could be seen on mammograms. 

  • Insight MMG marks areas of concern and generates a risk score of 0-100 for the presence of breast cancer (higher numbers are worse). 

Researchers decided to investigate the risk scores’ predictive power by applying Insight MMG to screening mammography exams acquired in the BreastScreen Norway program over three biennial rounds of screening from 2004 to 2018. 

  • They then correlated AI risk scores to clinical outcomes in exams for 116k women for up to six years after the initial screening round.

Major findings of the study included … 

  • AI risk scores were higher for women who later developed cancer, 4-6 years before the cancer was detected.
  • The difference in risk scores increased over three screening rounds, from 21 points in the first round to 79 points in the third round.
  • Risk scores had very high accuracy by the third round (AUC=0.93).
  • AI scores were more accurate than existing risk tools like the Tyrer-Cuzick model.

How could AI risk scores be used in clinical practice? 

  • Women without detectable cancer but with high scores could be directed to shorter screening intervals or screening with supplemental modalities like ultrasound or MRI.

The Takeaway
It’s hard to overstate the significance of the new results. While AI for direct mammography image interpretation still seems to be having trouble catching on (just like CAD did), risk prediction is a use case that could direct more effective breast screening. The study is also a major coup for Lunit, continuing a string of impressive clinical results with the company’s technology.

Breast Cancer Mortality Falls Again

Posted on by Brian Casey

New data from the American Cancer Society highlight the remarkable strides that have been made against breast cancer, with the U.S. death rate falling 44% over the last 33 years – saving over half a million lives. But the statistics also underscore the work that remains to be done, particularly with minority women. 

The fight against breast cancer has been one of public health’s major success stories.

  • High mammography screening uptake has led to early detection of cancers that can then be treated with revolutionary new therapies. 

Much of the credit for this success goes to the women’s health movement, which has conducted effective advocacy campaigns that have led to …

But breast cancer remains the third most common killer of women after heart disease and lung cancer, and there have been disturbing trends even as the overall death rate falls. 

  • Breast cancer incidence has been rising especially in younger women, and major disparities continue to be seen, particularly with survival in Black women.

The American Cancer Society’s new report represents the group’s biennial review of breast cancer statistics, finding … 

  • In 2024 there will be 311k new cases of invasive breast cancer, 56.5k cases of DCIS, and 42.3k deaths. 
  • The breast cancer mortality rate has fallen 44% from 1989 to 2022, from 33 deaths per 100k women to 19 deaths.
  • Some 518k breast cancer deaths have been averted.
  • The mortality rate ranges from 39% higher than average for Black women to 38% lower for Asian American Pacific Islander women. 
  • The mortality rate is slightly higher than average (0.5%) for White women.
  • The average breast cancer incidence rate is 132 per 100k women, but ranges from 5% higher for White women to 21% lower for Hispanic women.
  • Women 50 years and older will account for most invasive cases (84%) and deaths (91%).

The Takeaway

As Breast Cancer Awareness Month begins, women’s health advocates should be heartened by the progress that’s been made overall. But battles remain, from eliminating patient out-of-pocket payments for follow-up studies to addressing race-based disparities in breast cancer mortality. In many ways, the fight is just beginning. 

The Cost of Extra Cancer Detection

Posted on by Brian Casey

It’s well known that using additional screening modalities beyond traditional 2D mammography can detect more cancers in women with dense breast tissue. But at what cost? A new study in Clinical Breast Cancer documents both the clinical value and the economic cost of supplemental breast imaging technologies. 

2D mammography is the basis for any breast cancer screening program, but the modality’s shortcomings are well known, especially in women with dense breasts. 

  • In fact, the FDA earlier this month began requiring breast imaging providers to notify women of their density status and explain how higher density is a breast cancer risk factor. 

Imaging vendors and clinicians have developed a range of technologies to supplement 2D mammography when needed, ranging from DBT to molecular breast imaging to breast MRI.

  • Each has its own advantages and disadvantages, which can leave many breast imaging providers confused about the best technology to use.

To shed some light, Matthew Covington, MD, of the University of Utah compared detection rates for various supplemental imaging modalities; he then estimated costs for each if it was the only modality used for supplemental imaging with 2D mammography in a U.S. population with 469k detectable breast cancers. 

  • The study assumed that 2D mammography would detect only 41% of cancers – leaving the majority undetected. 

Adding a supplemental modality boosted cancer detection rates, but also screening’s cost …

  • DBT detected 47% of all cancers at a cost of $933M
  • Ultrasound detected 51% at a cost of $1.84B
  • MBI detected 71% at a cost of $4.16B
  • Contrast-enhanced mammography detected 80% at a cost of $3.87B
  • MRI detected 100% at a cost of $6.36B

As the data indicate, MRI is clearly the most effective supplemental modality, but at a cost that’s almost 7X that of DBT. 

The Takeaway

The new data are a fascinating – if sobering – look at the intersection of clinical value and economic cost. They also highlight healthcare’s inconvenient truth: The resources needed to provide the highest-quality care are finite, regardless of whether you’re in a single-payor or fee-for-service system.

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.

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