Latest news with #Lunit
Yahoo
7 days ago
- Business
- Yahoo
BreastScreen Australia Density Reporting Rule is a Critical Step for Women says Volpara Health, the Leader in AI-assisted Breast Density Measurement
Volpara software is used to assess the breast density of more than 1 million Australian womenannually WELLINGTON, New Zealand, June 2, 2025 /PRNewswire/ -- Volpara Health, a Lunit a company and global leader in software for the early detection of breast cancer, today applauded the significant advancement in breast health management with BreastScreen Australia's new position on breast density reporting. This new guidance recommends that all BreastScreen services in Australia provide breast density information to clients, enhancing awareness and understanding of this critical risk factor. A Step Toward More Accurate and Personalised Breast Cancer Screening Volpara's Scorecard software is utilised to measure the breast density of over 1 million women annually across Australia, including in BreastScreen Victoria and BreastScreen South Australia. This comprehensive, AI-driven approach supports physicians in making consistent, objective assessments, helping to ensure that breast cancer risk is more accurately evaluated for each patient. Volpara's volumetric breast density assessment software, including its proprietary Volpara® TruDensity™ AI algorithm, provides consistent and precise breast density measurements. Cleared by the Therapeutic Goods Administration (TGA), FDA, Health Canada, and CE-marked in Europe, this technology has been validated in more than 400 scientific articles and research abstracts, underscoring its reliability and accuracy. "We've been collaborating with leading clinicians and researchers worldwide for over a decade to make critical information about breast composition and its link to breast cancer more accessible," said Craig Hadfield, CEO, Volpara Health. "The new guidelines from BreastScreen Australia validate our focus on applying tech and AI advancements to breast density and are a significant step forward in enhancing the experience and understanding for women and their healthcare providers. We encourage women to contact their local healthcare provider and ask how they assess breast density." Understanding Breast Density: A Critical Risk Factor Breast density is an important factor in both breast cancer detection and risk assessment. It has been linked to an increased risk of developing breast cancer, while also making it more challenging to detect cancer through mammography. In Australia, approximately 40% of women have dense breasts, including 12% with extremely dense breasts. As breast density increases, the accuracy of mammography decreases. Research published in Radiology reveals that mammography can miss up to 50% of cancers in women with the densest breast tissue. Since both dense breast tissue and tumours appear white on a mammogram, cancers are often concealed, potentially delaying diagnosis. Studies confirm that early detection rates improve significantly when women with very dense breasts receive additional imaging, such as ultrasound or MRI, as part of their regular screening regimen. The Path Forward: Embracing Personalised Screening Breast density information is also integrated into several risk models, which helps identify women at higher risk of breast cancer who may benefit from supplemental screening. Volpara Scorecard is the only commercial automated software validated for use in both the Tyrer-Cuzick 8 and CanRisk (BOADICEA) riskmodels, providing consistent and robust breast density measurements. With this new guidance, Volpara is poised to play an essential role in supporting risk assessment and personalised screening in Australia, further advancing the goal of improving outcomes for women. The importance of personalised screening has also been recognised by other Australian health bodies, with Medicare recently introducing reimbursement for high-risk breast MRI screening based on Tyrer-Cuzick 8 or CanRisk risk assessment. This marks a growing recognition of the need for tailored breast cancer screening strategies that consider individual risk factors, including breast density. About Volpara Health Volpara Health is on a mission to save families from cancer with AI-powered software that helps healthcare providers better understand cancer risk, guide personalized care decisions, and recommend additional imaging and interventions. Used in over 3,500 facilities by more than 9,500 technologists worldwide. Volpara's software impacts nearly 18M patients, supports over 3.6M annual cancer risk assessments, and integrates seamlessly with electronic health records and imaging systems. Volpara helps radiologists quantify dense breast tissue with precision and technologists produce mammograms with optimal positioning, compression, and dose. Volpara software also streamlines operations to ease compliance and accreditation. Volpara, a Lunit company, is headquartered in Wellington, New Zealand, and has an office in Seattle. Volpara is the trusted partner of leading healthcare institutions globally. For more information, visit Logo - View original content: SOURCE Volpara Health Error in retrieving data Sign in to access your portfolio Error in retrieving data
Boston Globe
30-05-2025
- Health
- Boston Globe
AI-based mammography is here, and it has a trust problem
Enthusiasm is growing for the technology, as prospective trials conducted in Europe suggest that some AI tools can detect more cancers than radiologists alone. But beyond radiology networks that are implementing their own algorithms, many imaging centers and the radiologists who work for them are still skeptical. Advertisement 'It'll take time for us to really gain a lot of trust and confidence in it,' said Manisha Bahl, a breast radiologist at Massachusetts General Hospital, which is currently testing several AI-based tools for mammography in a head-to-head study. One large Advertisement Radiologists were much less likely to call patients for follow-up when their mammogram was flagged by the AI than when it was flagged by a human radiologist — even though the AI-flagged cases were more likely to be cancerous. If both members of a human-AI pair flagged a case for possible malignancy, only 39 percent of patients were called back. If it was two radiologists that flagged a case, callbacks shot up to 57 percent. The researchers used technology from South Korean developer Lunit, which also partly funded the study. 'Providing a superior performing algorithm doesn't actually necessarily improve human performance,' said Adam Rodman, a clinical reasoning researcher and practicing internist who co-directs the iMED initiative at Beth Israel Deaconess Medical Center. 'This study does a really good job at explaining why that is, and it's trust.' It's a common refrain for breast radiologists, who have been burned by technology before. Computer-aided detection tools for mammograms became standard starting in the 1980s, but in the long run, they never improved cancer detection or recall rates. 'We have to be very careful what we do with AI once it's out in the wild,' said Etta Pisano, chief research officer at the American College of Radiology, at the meeting of the Radiological Society of North America in December. Advertisement Performance for breast cancer AI — which marks or annotates suspicious lesions on a mammogram and provides a score or some indication of the likelihood of malignancy — is typically reported based on how well it identifies cancers in databases of old mammograms that have been previously screened by human radiologists. But in practice, that performance can vary significantly from center to center and radiologist to radiologist. The radiologist's level of trust, the way the software is integrated into their With all those variables, how can a radiologist make sure that they're using AI the most effective way? 'The answer is, nobody knows,' said Rodman. Normally, Bahl, the radiologist at Mass General, opens up a mammogram and interprets it entirely on her own before she activates any AI-based image processing. That's to cut down on the risk of automation bias — where she learns to rely too much on the machine. That approach makes sense for radiologists who trust their professional judgement more than a new tool, and it aligns with surveys of patients that suggest they typically want a doctor making the final call on image analysis, said Sanjay Aneja, a radiation oncologist at Yale Cancer Center who studies mammography AI. But that approach doesn't fulfill AI's other main promise: the potential for efficiency. 'If anything, it might be a little bit less efficient,' said Aneja. With skilled radiologists in shortage, AI will ideally help them work smarter and faster — which means looking at the algorithm's output upfront. Advertisement At radiology practices that are more aggressively leaning into mammogram AI, 'radiologists gain confidence and start to look earlier in the process,' said Chris McKinney, director of North American sales for Lunit. That kind of comfort doesn't emerge unless radiologists are getting regular practice with a new system — and adoption of the tools is inconsistent, since they aren't reimbursed by insurance. Instead, most practices deploying the tools ask patients to pay a cash fee of $40 to $90 for an AI add-on. At the top-adopting practice within Rezolut, a network of more than 40 US radiology practices that use Lunit's breast AI system, about 40 percent of patients opt to pay. 'Every radiologist is going to have to work with the AI product and get more comfortable with it and then utilize it individually,' said Stamatia Destounis, chair of the American College of Radiology's breast imaging commission. 'I don't think everyone's going to use it the same way.' But 'practice makes perfect' isn't enough for many AI researchers and physicians, who want more explicit guidance when it comes to appropriate deployment of these tools as they become more widely used. 'The problem is that there's actually very little guardrails with a lot of these devices as they get put out there,' said Aneja. Imaging quality can vary significantly between radiology practices, for example. 'There's very few algorithms that say, 'This image is of poor quality, can't evaluate it,'' said Aneja. 'We want an algorithm to be able to say what they don't know.' Advertisement Chiara Corti, an oncologist from Italy and clinical fellow at Dana-Farber, calls for more disclosure of the race and ethnicity of patients whose mammograms were used to train AI tools to ensure accuracy across groups. (ACR's AI The performance — and relative value — of an AI tool also depends on the radiologists who are using it. Not every radiologist is an expert in reading mammograms, and 'one of the biggest benefits of AI is to disseminate that level of knowledge,' said Aneja. To ensure radiology practices use this new generation of algorithms in a way that drives better cancer outcomes, they need more real-world, prospective research in the US 'We need to see those human interactions,' said Destounis. 'It's impossible to deduce from a study going back in time how every radiologist is going to interact with the AI system in practice, daily.' A 2024 Advertisement That kind of real-world evidence is one of the goals of a program out of ARPA-H called ACTR, for Advancing Clinical Trial Readiness. Pisano, the program's director, said at the December RSNA meeting that ACTR planned to test AI products in real-world, pragmatic trials out of imaging centers across the country, most likely starting with trials of breast cancer screening algorithms that could include hundreds of thousands of mammograms. The trials, she said at the time, were planned to start this spring. They have yet to be announced.
Korea Herald
28-05-2025
- Health
- Korea Herald
Lunit Launches Enhanced Lunit INSIGHT CXR4, Secures CE MDR Certification for Expanded AI Capabilities
Next-generation AI solution introduces new clinical features and broader disease detection for chest X-rays SEOUL, South Korea, May 28, 2025 /PRNewswire/ -- Lunit (KRX: a leading provider of AI for cancer diagnostics and therapeutics, today announced the official launch and CE certification under the Medical Device Regulation (MDR) of Lunit INSIGHT CXR4, its next-generation chest X-ray analysis solution. Lunit INSIGHT CXR4 is a comprehensive AI solution for chest imaging, designed to support radiologists across a wide range of clinical scenarios. It leverages advanced AI to detect 12 types of chest abnormalities—including lung nodules, pneumonia, and pneumothorax—expanding its diagnostic capabilities to include additional findings such as acute bone fractures. Trained on large-scale real-world datasets, the upgraded solution delivers improved diagnostic accuracy while supporting early detection of critical diseases. In addition to expanded disease detection, Lunit INSIGHT CXR4 introduces several new features designed to enhance clinical workflow and diagnostic support: With CE MDR certification, Lunit is now positioned to deploy INSIGHT CXR4 across Europe—offering enhanced diagnostic confidence and workflow efficiency for radiologists managing high imaging volumes. "With Lunit INSIGHT CXR4, we've gone beyond expanding detection—we've focused on what truly helps clinicians in their day-to-day workflow," said Brandon Suh, CEO of Lunit. "Features like active normal flagging and current-prior comparison are designed to reduce reading time and improve triage confidence, especially in high-volume settings. CE MDR certification is a key step toward broader adoption, and we're committed to bringing CXR4 to more hospitals worldwide." With CE MDR certification now in place, Lunit is preparing to pursue additional regulatory approvals to make CXR4 available in more regions. The company aims to further integrate its AI solutions into clinical workflows across diverse healthcare systems. CE MDR is the EU's enhanced regulatory standard for medical devices, ensuring stricter standards for safety, performance, and clinical validation. About Lunit Founded in 2013, Lunit (KRX: is a global leader in AI for cancer diagnostics and therapeutics. With a mission to conquer cancer through AI, Lunit develops AI-powered solutions for medical imaging and biomarker analysis to enable precise diagnosis and personalized treatment. Lunit's FDA-cleared Lunit INSIGHT suite supports cancer screening at over 4,800 medical institutions in more than 55 countries. Lunit clinical studies have been featured in top-tier journals—including The Lancet Digital Health and Journal of Clinical Oncology —and presented at major conferences such as ASCO and RSNA. Headquartered in Seoul with global offices, Lunit is driving the worldwide fight against cancer. Learn more at
Korea Herald
26-05-2025
- Health
- Korea Herald
Lunit Highlights AI's Role in Advancing Precision Oncology at ASCO 2025 with 12 Studies
SEOUL, South Korea, May 26, 2025 /PRNewswire/ -- Lunit (KRX: a leading provider of AI for cancer diagnostics and therapeutics, today announced that 12 studies featuring its AI-powered digital pathology solution will be presented at the 2025 American Society of Clinical Oncology (ASCO) Annual Meeting, taking place May 30–June 3 in Chicago, IL. Of these, 11 studies will be presented as posters and one as an online publication. One of the featured studies, conducted with Japan's National Cancer Center Hospital East (NCCE), evaluated HER2 expression in biliary tract cancer (BTC) patients using Lunit's AI-powered analyzer. The resulting AI scores showed strong agreement with pathologist-assigned IHC scores in a 288-patient screening cohort. Among 29 patients treated with trastuzumab deruxtecan (T-DXd), those with higher levels of HER2-intense tumor cells achieved a higher objective response rate (ORR) of 50%, along with significantly longer progression-free survival and overall survival. The study also found that AI-derived "membrane specificity" helped identify additional responders who achieved a 50% ORR and improved survival. This group included not only HER2-intense patients but also some traditionally classified as HER2-low, suggesting that the metric may expand the pool of patients who can benefit from T-DXd. These findings suggest that AI-powered HER2 analysis - especially when incorporating membrane specificity - could expand access to targeted treatment and enable more precise therapy selection in BTC. A separate prospective study conducted with NCCE evaluated the concordance between pathologist- and AI-assessed PD-L1 expression in lung cancer patients enrolled in LC-SCRUM, one of Japan's largest nationwide observational cohorts, using Lunit SCOPE PD-L1. The study included 847 non-small cell lung cancer (NSCLC) and 102 small cell lung cancer (SCLC) patients. The overall concordance between the AI model and three expert pathologists was 70%. Concordance was particularly high in key subgroups: 84% for TPS ≥50% and 94% for TPS 1–49%. Of the 416 patients initially classified as TPS <1% by pathologists, the AI identified 231 with higher PD-L1 expression. Since PD-L1 scoring is widely used to guide treatment eligibility, these results highlight the potential of AI-powered PD-L1 evaluation to uncover additional candidates for immunotherapy who may have been previously excluded based on low TPS. The high concordance with expert pathologists also reinforces the reliability of the AI model as a clinical decision-support tool. A third highlighted study introduced an AI model to predict CLDN18.2 expression in gastric cancer. CLDN18.2 is a therapeutic target for zolbetuximab. It is typically assessed using immunohistochemistry (IHC), which is often limited by tissue quantity, cost, and time. To address this, researchers trained the AI on H&E slides and validated it in the external cohort. The model achieved AUROCs over 0.751, suggesting the potential to efficiently pre-screen CLDN18.2-positive patients using only H&E slides. The study also analyzed immune phenotypes using AI-powered whole-slide image analysis to explore treatment implications. Among patients predicted to be CLDN18.2-negative, those with an "inflamed" phenotype—marked by high tumor-infiltrating lymphocyte (TIL) density—showed significantly better outcomes when treated with immune checkpoint inhibitor plus chemotherapy compared to chemotherapy alone. These findings suggest that combining AI-based CLDN18.2 prediction with immune phenotype analysis could guide first-line treatment decisions without additional IHC tests. "Our ASCO 2025 presentations build on years of work to turn AI into a clinically dependable tool—not just for reading pathology images, but for improving how we select the right treatments. From HER2 scoring in biliary tract cancer to PD-L1 evaluation in lung cancer, our models are helping uncover treatment opportunities for patients who might otherwise be overlooked. This level of precision and reproducibility is exactly what AI needs to deliver real clinical value," said Brandon Suh, CEO of Lunit. In addition to these three featured studies, Lunit will present 9 additional abstracts covering a wide range of research topics. These include AI-based subcellular profiling to assess the drug-targetability of 74 membrane proteins across 34 cancer types, and deep learning analysis of endothelial cells to understand how the tumor vascular environment influences immunotherapy response. Lunit will be exhibiting at Booth #26149, where attendees can learn more about the studies and AI solutions featured at this year's ASCO. Lunit's featured presentations at ASCO 2025 include: ### About Lunit Founded in 2013, Lunit (KRX: is a global leader in AI for cancer diagnostics and therapeutics. With a mission to conquer cancer through AI, Lunit develops AI-powered solutions for medical imaging and biomarker analysis to enable precise diagnosis and personalized treatment. Lunit's FDA-cleared Lunit INSIGHT suite supports cancer screening at over 4,800 medical institutions in more than 55 countries. Lunit clinical studies have been featured in top-tier journals—including The Lancet Digital Health and Journal of Clinical Oncology —and presented at major conferences such as ASCO and RSNA. Headquartered in Seoul with global offices, Lunit is driving the worldwide fight against cancer. Learn more at
Korea Herald
19-05-2025
- Business
- Korea Herald
Lunit Surpasses 1 Million Mammograms in U.S. One Year After Volpara Acquisition
Building on Volpara's breast health expertise, Lunit strengthens its U.S. presence—now delivering AI-powered breast cancer screening across 200 imaging sites SEOUL, South Korea, May 19, 2025 /PRNewswire/ -- Lunit (KRX: a leading provider of AI for cancer diagnostics and therapeutics, today announced major milestones in the United States, one year after its acquisition of Volpara Health Technologies. Since acquiring Volpara, Lunit has significantly expanded its presence across North America. By June 2025, Lunit's AI solutions for digital breast tomosynthesis (Lunit INSIGHT DBT) and mammography analysis (Lunit INSIGHT MMG) will be deployed at over 200 imaging centers and hospitals across the U.S., with approximately 350 to 400 radiologists utilizing its technology. Together, Lunit and Volpara now power more than one million annual mammograms across North America — a major step forward in the company's U.S. growth strategy. Lunit's growing customer base includes leading healthcare providers such as Rezolut, SimonMed Imaging, and UC Davis Health, reflecting its strengthening position in the U.S. medical imaging market. In addition to its expanding U.S. footprint, Lunit and Volpara introduced the concept of a comprehensive Ecosystem for Cancer Detection and Care at RSNA 2024. The Ecosystem is designed to unify AI technologies for lesion detection, workflow optimization, quality control, and patient engagement—empowering clinicians with actionable insights and scalable solutions across the cancer care continuum. Lunit and Volpara also demonstrated the complementary value of combining Lunit INSIGHT Risk and Volpara Risk Pathways at RSNA 2024—offering a glimpse into how imaging-based and clinical data may be used together to personalize breast cancer risk assessment. The two solutions were shown as a prototype to work in tandem, that would predict the likelihood of breast cancer development within 1 to 5 years, to enable earlier interventions, more personalized care, and redefine standards for breast cancer screening and management. "The successful integration of Lunit and Volpara has accelerated our mission to transform cancer diagnostics globally, and the momentum we've built in the United States marks just the beginning," said Brandon Suh, CEO of Lunit. "By combining Lunit's AI innovation with Volpara's breast health expertise, we're delivering a new standard of comprehensive support for clinicians—enhancing early detection, improving screening quality, optimizing workflows, and ultimately helping providers deliver better outcomes for patients worldwide. With our expanded footprint and comprehensive ecosystem, we are poised to lead the future of cancer care." Lunit plans to further expand its U.S. operations, building on its strong foundation with next-generation AI solutions, dedicated clinical support, and a growing ecosystem designed to shape the future of cancer detection and care. Founded in 2013, Lunit (KRX: is a global leader in AI for cancer diagnostics and therapeutics. With a mission to conquer cancer through AI, Lunit develops AI-powered solutions for medical imaging and biomarker analysis to enable precise diagnosis and personalized treatment. Lunit's FDA-cleared Lunit INSIGHT suite supports cancer screening at over 4,800 medical institutions in more than 55 countries. Lunit clinical studies have been featured in top-tier journals—including The Lancet Digital Health and Journal of Clinical Oncology —and presented at major conferences such as ASCO and RSNA. Headquartered in Seoul with global offices, Lunit is driving the worldwide fight against cancer. Learn more at
