Tag: breast cancer

Risk-Based Mammography Screening Returns

Posted on by Brian Casey

The idea of risk-based mammography screening is back with the publication of a new study in JAMA Network Open claiming that some risk-based strategies averted more breast cancer deaths with fewer false positives than age-based criteria. But like a previous paper on risk-based screening, the new findings raise concerns.

The idea behind risk-based screening is to focus healthcare resources on the people who need them most while sparing low-risk individuals from unnecessary medical interventions.

  • But risk-based breast cancer screening needs more clinical validation before it can be adopted broadly. This was tried with the WISDOM study, but researchers found no statistically significant difference in biopsy rates and only a modest reduction in mammograms performed.

A slightly different tack was taken with the new study, which compared conventional age-based biennial screening to a package of risk-based approaches based on a patient’s five-year breast cancer risk as calculated by widely accepted techniques like the Gail model and BCSC calculator.

  • Out of 50 risk-based strategies, nine averted more deaths than biennial age-based screening for women aged 40-74 (both were compared to no screening), and resulted in fewer false-positive recalls.

One such strategy highlighted by the authors used no screening for younger low-risk women, biennial screening for average-risk women, and annual screening for intermediate- and high-risk women, with the following results…

  • 6% more breast cancer deaths averted per 1k women versus conventional screening (7.2 vs. 6.8).
  • 8% fewer false-positive recalls (1,257 vs. 1,365).
  • While other risk-based strategies saw death reductions as high as 7.5 deaths per 1k women and false-positive reductions of 8-23%.

One key thing to note with the new study is its use of biennial screening as the control group, in line with current USPSTF recommendations for women aged 40-74. 

  • But many clinical organizations like ACR, ACOG, SBI, and NCCN recommend annual screening, and the new study’s findings may have been very different if compared to an annual model.

The Takeaway

This week’s findings are generally more supportive of risk-based screening than those of last year’s WISDOM study. But the new paper’s marginal improvement in cancer deaths averted might disappear when compared with annual age-based mammography. And like WISDOM, its use of clinical models for risk prediction may soon be obsolete given rapid developments in AI-based risk assessment. 

Breast Density’s Impact on Mammography

Posted on by Brian Casey

Breast density has a well-known effect on the accuracy of mammography screening – and it’s not a positive one. But a new study in Academic Radiology sheds light on density’s impact thanks to a massive patient population and its use of digital breast tomosynthesis, the most current breast screening technology.

Breast density is known to reduce the effectiveness of X-ray mammography by obscuring suspicious areas and making cancers harder to find. 

  • Women with dense breast tissue are typically directed to other imaging modalities for screening, such as ultrasound, breast MRI, and contrast-enhanced mammography.

The problem posed by breast density is significant enough that in 2024 the FDA implemented new MQSA rules requiring women getting screening mammograms to be notified of their density status.

  • It’s particularly important because having dense breast tissue is also a risk factor for breast cancer.

In the new study, MGH researchers aimed to quantify exactly how much breast density affects mammography screening through a large patient population screened with DBT, the state of the art in the U.S.

  • Researchers included 111.1k women who got DBT exams from 2013 to 2019 at their institution. 

They then calculated important metrics like sensitivity and specificity, as well as cancer detection and false-negative rates, across the four categories of dense breast tissue, from entirely fatty (A) to extremely dense (D), finding…

  • Sensitivity was lowest in extremely dense tissue compared to entirely fatty (62% vs. 93%).
  • Specificity was also lower for extremely dense and heterogeneously dense categories compared to entirely fatty (93% for both vs. 97%).
  • The false-negative rate for extremely dense tissue was over 8X that of entirely fatty based on adjusted odds ratio (aOR = 8.35).
  • While the abnormal interpretation rate was 57% higher for extremely dense versus entirely fatty tissue.

The Takeaway

The new findings are some of the most definitive yet on the negative effect breast density has on screening mammography’s accuracy and support the FDA’s 2024 notification requirement. They hopefully will spur development of new technologies to mitigate density’s impact. 

Risk-Based Mammo Screening – Ready for Prime Time?

Posted on by Brian Casey

Is mammography screening based on patient risk ready to take over for age-based screening? Results from the WISDOM study presented at last week’s San Antonio Breast Cancer Symposium and published simultaneously in JAMA suggest that while risk-based screening has its merits, more work may need to be done. 

Cancer screening exams like mammography have reduced disease-specific mortality, but (with the exception of lung cancer screening) all use exclusively age-based criteria to determine who should get screened.

  • Age isn’t a great tool for determining who’s at higher risk of getting cancer, but it’s the best tool we’ve had – up to now.

New cancer risk prediction tools are now becoming available, prompting debate over whether these techniques could make screening more precise by directing it to those most at risk.

  • Higher-risk people could get more frequent screening, while lower-risk individuals might be directed to longer screening intervals.

The WISDOM study presented at SABCS 2025 investigates this question. WISDOM is a randomized clinical trial that compared risk-based breast screening to age-based annual screening in 28.4k women followed for five years. 

  • Risk categorization was performed with genetic testing, polygenic risk scores, and BCSC scores, which incorporate family history and imaging results. 

Women in the risk-based screening group were directed into one of four screening strategies, from alternating mammography and MRI every six months for high-risk women to no screening until age 50 for low-risk women.

  • The study’s primary outcomes were detection rates for breast cancers rated as stage IIB or higher and effectiveness in reducing biopsy rates – a proxy for screening-caused morbidity.

Across the study population, researchers found…

  • The rate of mammograms per 100k person-years was lower in the risk-based cohort compared to age-based screening (43.1k vs. 46.9k). 
  • The rate of stage IIB or higher cancers per 100k person-years was also lower in the risk-based cohort (30 vs. 48).
  • But there was no statistically significant difference in biopsy rates, with a rate difference of 99 per 100k person-years (p = 0.10).

One problem with the WISDOM trial was that the actual screening exams were performed outside the study, and some patients did not comply with screening recommendations, potentially confounding results. 

The Takeaway

The WISDOM authors concluded that a risk-based screening approach is safe, but the lack of a difference in biopsy rates makes one wonder if veering from established age-based criteria is worth it. In any event, the coming arrival of risk stratification based on AI mammogram analysis could make the genetic testing-based approach used in WISDOM obsolete.

Mammo Screening Deserts Limit Access

Posted on by Brian Casey

It’s no secret that there are sharp regional differences in healthcare access in the U.S. But a new report puts a price on the access problem as it pertains to mammography – nearly 10k additional cases of breast cancer a year due to limited access in “cancer screening deserts” that don’t have mammography equipment. 

Mammography has been a success story among population-based cancer screening tests. 

  • The widespread implementation of breast screening in the 1980s is generally credited – along with improved treatments – with reducing breast cancer mortality by 44% from 1982 to 2022.

But breast cancer is still a lethal disease, killing 42k women a year in the U.S.

  • And screening’s benefits have not been distributed equally, with women in rural areas and those with lower socioeconomic status having lower completion rates.

What would it take to even out the differences? To answer this question, researchers from the Milken Institute analyzed the U.S. mammography installed base at the county level. 

  • They then correlated machine distribution with county population as well as cancer detection rates to find out how efficiently different counties were performing. 

They discovered…

  • High regional variation in mammography machine distribution.
  • The lowest distribution was in the Southwest and southern Midwest while the highest was in major urban areas, particularly on the coasts.
  • 890 counties did not have mammography machines.
  • Counties with the most mammography machines had 7.5% higher breast cancer incidence rates per 100k women compared to counties with no machines (329 vs. 306) – a sign they were detecting more cancers. 
  • There were 155 counties where mammography machine deployment would have the biggest return. 
  • And 9.6k breast cancer cases would be detected if counties with low or no mammography capacity detected breast cancer at the same rate as high-detection counties.

The new results track with another recent study that also revealed the presence of cancer screening deserts in the Southwest.

So what can be done? The Milken researchers proposed that low-resource counties be targeted for investment, but simply installing new machines won’t by itself cure the access problem. 

  • It’s also important to address barriers such as language, transportation, and cost-sharing in order to achieve equal access. 

The Takeaway

The new report shows that mammography access isn’t just an abstract issue – it’s one that is claiming the lives of thousands of U.S. women a year. Fortunately, the Milken researchers have done much of the legwork in identifying the specific areas that deserve attention. 

Hologic to Go Private in $18.3B Buyout

Posted on by Brian Casey

Women’s imaging vendor Hologic will go private in an $18.3B buyout led by two private equity firms, Blackstone and TPG. The move is easily the largest acquisition in radiology this year – the question is how it will impact one of the biggest corporate success stories in women’s health. 

Hologic has a long history in medical imaging and was founded in 1985 to develop and market bone densitometry systems. It soon expanded into mammography, molecular diagnostics, and women’s health treatments.

  • The company went public in 1990, and has maintained its independence even as radiology underwent a period of consolidation in the 1990s and 2000s that saw most mid-cap firms get acquired by multinational OEMs.

Much of Hologic’s momentum was driven by the conversion of U.S. mammography facilities from standard 2D mammography to 3D digital breast tomosynthesis. 

  • This shift was led by Hologic’s Selenia Dimensions system, which in 2011 was the first DBT system to get FDA approval. Hologic rode its momentum to a U.S. mammography installed base market share approaching 70%. (Signify Research estimates Hologic currently has a 34% market share of the global mammography market.)

But as often happens to many market leaders, Hologic’s position began slipping in recent years. 

  • The multinational OEMs have improved their positions in women’s imaging, releasing DBT systems that are more competitive with Hologic’s offerings while also benefiting from multiyear purchasing agreements with large health systems in which mammography systems can be bundled with CT, MRI, and other equipment. 

Perhaps as a result, Hologic’s Breast Health segment has become a drag on revenue growth due to lower equipment sales. Breast Health revenues for the most recent Q3 period fell 5.8%, following a 6.9% drop in Q2 and a 2.1% decline in Q1. 

  • Indeed, reports began surfacing in May 2025 that Blackstone and TPG were targeting Hologic for acquisition, with Hologic reportedly rejecting a $16.7B offer. 

The bid was apparently sweetened, with an acquisition price of $79 a share, a 46% premium from before the acquisition rumors started, for a total value of $18.3B. The buyout should close in the first half of calendar 2026.

The Takeaway

Hologic built itself into a radiology success story through a combination of technological innovation and an obsessive focus on a single market segment – women’s health. The question is whether that focus will continue under its new PE-led ownership.

Perils of Missed Mammography

Posted on by Brian Casey

Yet another study is illustrating the perils of missing mammography screening. New research in JAMA Network Open found that women diagnosed with breast cancer who missed their previous screening exam had signs of delayed diagnosis and worse clinical outcomes. 

Mammography screening is generally credited – along with improved treatments – with a steady decline in breast cancer death rates since the start of population-based breast screening.

  • But most studies on mammography’s effectiveness tend to compare women who participated regularly in screening with those who never did. 

That’s not really a realistic comparison these days, as mammography’s relatively high compliance rate means that most women are getting screened at least some of the time.

  • But what happens if women miss a screening exam? In a BMJ study published last month, researchers found that women who missed their first screening exam had a 40% higher risk of breast cancer death.

In the current study, researchers took a slightly different tack, looking at 8.6k women in Sweden whose breast cancer was detected on screening exams starting in 2015. 

  • In all, 17% of women missed the screening exam immediately before their cancer diagnosis. 

Compared to women who attended all screening rounds, those who missed their previous exam had higher adjusted odds ratio for…

  • Larger tumors ≥ 20 mm (AOR = 1.55).
  • Lymph node involvement (AOR = 1.28).
  • Distant metastasis (AOR = 4.64).
  • Worse breast cancer-specific survival (AOR = 1.33).
  • Lower 20-year breast cancer-specific survival (86% vs. 89%). 

What’s more, the program’s cancer detection rate per 1k screenings was sharply higher in the second screening round for women who missed the first round (7.35 vs. 5.59). 

  • This is most likely a sign that cancers that could have been detected in the first round instead were detected in the second round – another sign of delayed diagnosis.

Women who had missed their previous screening tended to be younger, unemployed, unmarried, and born outside of Sweden, and also had lower income. 

  • Women with these characteristics could be targeted for more intensive outreach, such as shorter invitation intervals or outreach after a missed appointment. 

The Takeaway

The new study once again highlights the importance of regular mammography screening in detecting breast cancer. Even one missed exam can have serious clinical consequences – highlighting the importance of identifying and contacting women who might be more prone to missed appointments.

Missing Breast Screening Boosts Death Risk

Posted on by Brian Casey

Missing a first breast cancer screening exam can be hazardous to your health. A new study in BMJ found that women who missed their first mammography screening had a 40% higher long-term risk of breast cancer death. 

Mammography screening has been shown to prevent breast cancer deaths by detecting cancer earlier, when it can be treated more effectively.

  • But breast screening adherence rates still aren’t as high as they should be, leaving women’s health advocates to wonder what they can do to spur better compliance.

In the new study, researchers investigated whether mammography compliance itself could be an early warning sign that women might not be taking screening seriously enough.

  • They analyzed data on 433k women invited to the Swedish Mammography Screening Programme from 1991 to 2020 and correlated clinical outcomes over 25 years with whether or not patients completed their first screening exam (32% didn’t).

Compared to women who missed their first mammography appointment, women who followed through with their exam…

  • Had a 40% lower risk of dying from breast cancer. 
  • Had lower breast cancer mortality rates per 1k women (7 vs. 9.9). 
  • Got nearly twice as many breast screenings over the study period (8.7 vs. 4.8 screenings).
  • Had similar breast cancer incidence rates (7.8% vs. 7.6%), a sign that non-participation delayed detection rather than increased incidence. 

What’s more, women who missed their first appointment were 32% more likely to have invasive cancer and had higher odds ratios for stage III and stage IV disease (OR = 1.53 and 3.61, respectively). 

Researchers concluded that women who missed their first mammography appointment were also more likely to miss future ones – putting them at higher risk of breast cancer death.

  • But a missed initial appointment also could serve as a warning to women’s health centers that these patients deserve extra attention, through tools as simple as more provider outreach or automatically scheduled second appointments. 

The Takeaway

The new findings offer – yet again – more support for the effectiveness of population-based breast screening in reducing breast cancer deaths. What’s novel is that they show that non-participation is an early warning sign that could activate a slate of more aggressive outreach measures to bring these women in. 

Lunit Acquires Prognosia Breast Cancer Risk AI

Posted on by Brian Casey

AI developer Lunit is ramping up its position in breast cancer risk prediction by acquiring Prognosia, the developer of a risk prediction algorithm spun out from Washington University School of Medicine in St. Louis. The move will complement Lunit and Volpara’s existing AI models for 2D and 3D mammography analysis. 

Risk prediction has been touted as a better way to determine which women will develop breast cancer in coming years, and high-risk women can be managed more aggressively with more frequent screening intervals or the use of additional imaging modalities.

  • Risk prediction traditionally has relied on models like Tyrer-Cuzick, which is based on clinical factors like patient age, weight, breast density, and family history.

But AI advancements have been leveraged in recent years to develop algorithms that could be more accurate than traditional models.

  • One of these is Prognosia, founded in 2024 based on work conducted by Graham Colditz, MD, DrPH, and Shu (Joy) Jiang, PhD, at Washington University.

Their Prognosia Breast algorithm analyzes subtle differences and changes in 2D and 3D mammograms over time, such as texture, calcification, and breast asymmetry, to generate a score that predicts the risk of developing a new tumor.

Prognosia built on that momentum by submitting a regulatory submission to the FDA, and the application received Breakthrough Device Designation.

  • In conversations with The Imaging Wire, Colditz and Jiang believe AI-based estimates like those of Prognosia Breast will eventually replace the one-size-fits-all model of breast screening, with low-risk women screened less often and high-risk women getting more attention.

Colditz and Jiang are working with the FDA on marketing authorization, and once authorized Prognosia’s algorithm will enter a segment that’s drawing increased attention from AI developers.

  • The two will continue to work with Lunit as it moves Prognosia Breast into the commercialization phase and integrates the product with Lunit’s own offerings like the RiskPathways application in its Lunit Breast Suite and technologies it accessed through its acquisition of Volpara in 2024. 

The Takeaway

Lunit’s acquisition of Prognosia portends exciting times ahead for breast cancer risk prediction. Armed with tools like Prognosia Breast, clinicians will soon be able to offer mammography screening protocols that are far more tailored to women’s risk profiles than what’s been available in the past. 

Ensemble Mammo AI Combines Competing Algorithms

Posted on by Brian Casey

If one AI algorithm works great for breast cancer screening, would two be even better? That’s the question addressed by a new study that combined two commercially available AI algorithms and applied them in different configurations to help radiologists interpret mammograms.

Mammography AI is emerging as one of the primary use cases for medical AI, understandable given that breast imaging specialists have to sort through thousands of normal cases to find one cancer. 

Most of these studies applied a single AI algorithm to mammograms, but multiple algorithms are available, so why not see how they work together? 

  • This kind of ensemble approach has already been tried with AI for prostate MRI scans – for example in the PI-CAI challenge – but South Korean researchers writing in European Radiology believed it would be a novel approach for mammography.

So they combined two commercially available algorithms – Lunit’s Insight MMG and ScreenPoint Medical’s Transpara – and used them to analyze 3k screening and diagnostic mammograms.

  • Not only did the authors combine competing algorithms, but they adjusted the ensemble’s output to emphasize five different screening parameters, such as sensitivity and specificity, or by having the algorithms assess cases in different sequences.

The authors assessed ensemble AI’s accuracy and ability to reduce workload by triaging cases that didn’t need radiologist review, finding…

  • Outperformed single-algorithm AI’s sensitivity in Sensitive Mode (84% vs. 81%-82%) with an 18% radiologist workload reduction.
  • Outperformed single-algorithm AI’s specificity in Specific Mode (88% vs. 84%-85%) with a 42% workload reduction.
  • Had 82% sensitivity in Conservative Mode but only reduced workload by 9.8%.
  • Saw little difference in sensitivity based on which algorithm read mammograms first (80.3% and 80.8%), but both approaches reduced workload 50%.

The authors suggested that if applied in routine clinical use, ensemble AI could be tailored based on each breast imaging practice’s preferences and where they felt they needed the most help.

The Takeaway

The new results offer an intriguing application of the ensemble AI strategy to mammography screening. Given the plethora of breast AI algorithms available and the rise of platform AI companies that put dozens of solutions at clinicians’ fingertips, it’s not hard to see this approach being put into clinical practice soon.

Mammo Risk Prediction Improves with AI

Posted on by Brian Casey

Artificial intelligence is beginning to show that it can not only detect breast cancer on mammograms, but it can predict a patient’s future risk of cancer. A new study in JAMA Network Open showed that a U.S. university’s homegrown AI algorithm worked well in predicting breast cancer risk across diverse ethnic groups. 

Breast cancer screening traditionally has used a one-size-fits-all model based on age for determining who gets mammography.

  • But screening might be better tailored to a woman’s risk, which can be calculated from various clinical factors like breast density and family history.

At the same time, research into mammography AI has uncovered an interesting phenomenon – AI algorithms can predict whether a woman will develop breast cancer later in life even if her current mammograms are normal. 

The new study involves a risk prediction algorithm developed at Washington University School of Medicine in St. Louis that uses AI to analyze subtle differences and changes in mammograms over time, including texture, calcification, and breast asymmetry.

  • The algorithm then generates a mammogram risk score that can indicate the risk of developing a new tumor.

In clinical trials in British Columbia, the algorithm was used to analyze full-field digital mammograms of 206.9k women aged 40-74, with up to four years of prior mammograms available. Results were as follows …

  • The algorithm had an AUROC of 0.78 for predicting cancer over the next five years.
  • Performance was higher for women older than 50 compared to 40-50 (AUROC of 0.80 vs. 0.76).
  • Performance was consistent across women of different races.
  • 9% of women had a five-year risk higher than 3%. 

The algorithm’s inclusion of multiple mammography screening rounds is a major advantage over algorithms that use a single mammogram as it can capture changes in the breast over time. 

  • The model also showed consistent performance across ethnic groups, a problem that has befallen other risk prediction algorithms trained mostly on data from White women. 

The Takeaway

The new study advances the field of breast cancer risk prediction with a powerful new approach that supports the concept of more tailored screening. This could make mammography even more effective than the one-size-fits-all approach used for decades.

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