Author: Brian Casey

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.

MRI Reduces Prostate Biopsies

Posted on by Brian Casey

New research provides additional support for MRI’s role in making prostate screening more effective. In a new study in NEJM, researchers found that MRI can help reduce unnecessary biopsies more than 50%, with a very low chance of missing high-risk disease. 

As we’ve discussed in previous newsletters, prostate cancer screening based on PSA levels is an imprecise test. 

  • Many men with suspiciously high PSA (typically 3-4 ng/mL or higher) undergo biopsies that detect clinically insignificant disease that would never present a health risk during their lifetimes – the classic definition of overdiagnosis. 

Adding MRI can help make prostate screening more precise by directing biopsy-based workup to only those men with clinically significant cancer – but questions still abound about exactly when it should be used. 

In new results from the GÖTEBORG-2 trial in Sweden, researchers compared prostate screening protocols in men with PSA levels 3 ng/mL and higher who got MRI scans:

  • One group automatically got systemic biopsy and then MRI-targeted biopsy based on MRI results.
  • The other group only got MRI-targeted biopsy if they had a suspicious MRI scan.

In 13.2k men who were followed up for a median of four years, researchers found that those in whom systemic biopsy was omitted …

  • Had 57% lower risk of clinically insignificant cancers.
  • Had lower relative risk of clinically insignificant cancers in subsequent screening rounds (RR=0.25 vs. 0.49).
  • Had 16% lower risk of detecting clinically significant cancers.
  • Had 35% lower risk of advanced or high-risk cancers.

On the down side, the protocol eliminating systemic biopsy could lead to later diagnoses for higher-risk disease for 3 in 1k men – but given the slow-growing nature of prostate cancer it’s not clear how significant this is. 

  • Also, the data indicate that “most prostate cancers become visible on MRI” before they are incurable, which increases the likelihood that they would at least be detected on subsequent screening rounds and could be treated effectively.

The Takeaway

The new findings should help clinicians hone in on the best prostate screening protocols for maximizing detection of clinically significant cancer while minimizing unnecessary workup. Hopefully, the addition of new technologies like AI can move this process along.

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.

Lung Screening’s Star Turn at WCLC 2024

Posted on by Brian Casey

The World Conference on Lung Cancer (WCLC) is underway in San Diego this week, and CT lung cancer screening has had a starring role at the meeting. The sessions come as lung screening continues to build momentum through 2024. 

Low-dose CT lung screening got the green light from the USPSTF over a decade ago, but screening rates are still mired in the single digits in many regions. 

  • The evidence backing LDCT’s life-saving value has been building, however, and around the world countries are launching national screening programs to counter the smoking epidemic, the leading cause of preventable cancer death worldwide.

Sessions at WCLC 2024 have highlighted this progress, with many speakers focusing on ways to boost screening compliance or use tools like AI to detect more lung cancers. 

Presentations on early lung cancer detection have included the following findings… 

  • Three years of lung screening starting in 2021 in Quebec produced a lung cancer detection rate of 1.6% in the first screening round, with 85% of cancers stage I or II.
  • Advanced practitioner nurses are being trained in Australia to assess pulmonary nodules to alleviate workforce challenges when the country’s national lung screening program starts in July 2025. 
  • Using Coreline Soft’s AVIEW algorithm to read baseline LDCT exams helped BioMILD researchers move to a triennial screening interval without missing cancers. 
  • The QUILS system for lung cancer quality assurance helped assess quality across multiple LDCT screening sites in Kentucky.
  • Over 10 years in which 2.3k patients were scanned, researchers found a 3.7% lung cancer detection rate and 100% survival for early-stage cancer.
  • Among 4.2k patients, those who got screened had more stage I-II disease (72% vs. 37%) and higher rates of surgery-only treatment (56% vs. 25%) at three years. 
  • Using PanCan criteria to manage suspicious lung nodules worked better than Lung-RADS in 4.5k people screened, with fewer workup referrals (2.8% vs. 7.4%) and better PPV for high-risk malignancy (48% vs. 18%).

The Takeaway

This is just a selection of the exciting research being presented at WCLC 2024. It seems evident that CT lung screening’s future as a mainstream cancer test is closer than ever.

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