Odense University Hospital

Two new studies show Lunit INSIGHT MMG cuts radiologist workload by 50% in a double reading environment while maintaining or improving screening quality, and predicts cancer risk up to six years in advance SEOUL, South Korea, Oct. 23, 2024 /PRNewswire/ -- Amid rising demands and a global shortage of radiologists, Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced pivotal findings from two large-scale studies evaluating its AI-powered mammography solution, Lunit INSIGHT MMG. The studies conducted by researchers at the Cancer Registry of Norway and Odense University Hospital in Denmark demonstrate the technology's potential to significantly improve breast cancer screening programs. Continue Reading Lunit INSIGHT MMG, Lunit's AI-powered mammography analysis solution, showcased its ability to predict breast cancer risk and assist in streamlining radiologist workflows. The Norwegian study, published in JAMA Network Open, led by Professor Solveig Hofvind, evaluated how Lunit INSIGHT MMG could estimate the development of future cancer. The research analyzed data from 116,495 women aged 50-69 who underwent at least three consecutive biennial screening rounds at 9 breast centers in Norway. The research found that Lunit INSIGHT MMG can estimate future breast cancer risk up to 4 to 6 years before it becomes detectable. Lunit's AI assigns each breast a score from 0 to 100, with higher values indicating a greater likelihood of cancer being present on the current mammogram. The study found that the mean AI scores were consistently higher for breasts that developed cancer compared to those that remained cancer-free, even 4 to 6 years before detection. Over multiple screening rounds, the gap in these AI scores between breasts that developed cancer and those that did not grew larger, indicating the AI's ability to detect subtle signs of cancer earlier. Specifically, for women who developed screen-detected cancer, the mean absolute differences in AI scores between the two breasts were 21.3 in the first study round, 30.7 in the second, and 79.0 in the third. Additionally, Lunit's AI achieved an Area Under the Curve (AUC) for the absolute difference of 0.63, 0.72, and 0.96 across the three screening rounds for screen-detected cancer. These findings indicate that Lunit's AI can effectively identify early signs of cancer and offer a pathway for personalized screening approaches. The double reading of mammograms, though the standard of care in Denmark and other European regions, is resource-intensive and demands significant radiologist time. The Danish study, led by Dr. Mohammad T. Elhakim and published in Radiology: Artificial Intelligence, explored how Lunit INSIGHT MMG could enhance double reading without compromising screening performance. The research team evaluated three AI-integrated screening scenarios on 249,402 mammograms, demonstrating significant reductions in radiologist workload while either maintaining or improving accuracy (Scenario 1: AI replaced first reader; Scenario 2: AI replaced second reader; Scenario 3: AI replaced both for triaging of low and high-risk cases). Results showed that replacing the first, second, or both radiologists with Lunit's AI could reduce reading volume by 48.8%, 48.7%, and 49.7%, respectively, while maintaining or improving cancer detection accuracy. Most notably, the AI triage scenario (Scenario 3) achieved higher sensitivity, positive and negative predictive values with a lower arbitration rate when compared to standard double reading. This result highlights that by utilizing Lunit INSIGHT MMG, healthcare systems can significantly reduce radiologists' burden without compromising the screening program's diagnostic performance quality, allowing them to focus on more complex cases and improving overall efficiency. "These studies provide compelling evidence of our AI's potential to transform breast cancer screening," said Brandon Suh, CEO of Lunit. "In Norway, our AI identified future breast cancer risk up to 6 years ahead, providing women and healthcare providers valuable time for proactive interventions. The Danish study showed a 50% reduction in radiologist workload without sacrificing detection quality—a result that directly impacts radiology efficiency worldwide. These are not hypothetical improvements; they represent concrete benefits that healthcare providers and patients are experiencing today. Our focus is on ensuring these advancements translate directly to better outcomes for women undergoing breast cancer screening." About Lunit Founded in 2013, Lunit (KRX:328130.KQ) is a medical AI company on a mission to conquer cancer. We harness AI-powered medical image analytics and AI biomarkers to ensure accurate diagnosis and optimal treatment for each cancer patient. Our FDA-cleared Lunit INSIGHT suite for cancer screening serves over 3,500 hospitals and medical institutions across 50+ countries. Our clinical studies have been published in top journals, including the Journal of Clinical Oncology and the Lancet Digital Health, and presented at global conferences such as the ASCO and RSNA. In 2024, Lunit acquired Volpara Health Technologies, setting the stage for unparalleled synergy and accuracy, particularly in breast health and screening technologies. Headquartered in Seoul, South Korea, with a network of offices worldwide, Lunit leads the global fight against cancer. Discover more at lunit.io. SOURCE Lunit WANT YOUR COMPANY'S NEWS FEATURED ON PRNEWSWIRE.COM? 440k+ Newsrooms & Influencers 9k+ Digital Media Outlets 270k+ Journalists Opted In GET STARTED

TUESDAY, Sept. 3, 2024 -- In patients with newly diagnosed type 2 diabetes, baseline interleukin-6 (IL-6) levels may enhance prediction of new-onset obesity-related cancers, according to a study scheduled to be presented at the annual meeting of the European Association for the Study of Diabetes, being held from Sept. 9 to 13 in Madrid. Mathilde Dahlin Bennetsen, from the Steno Diabetes Center Odense at Odense University Hospital in Denmark, and colleagues explored whether IL-6, tumor necrosis factor-alpha (TNF-α), and high-sensitivity C-reactive protein (hsCRP) can serve as predictive biomarkers for obesity-related cancers in patients recently diagnosed with type 2 diabetes. The analysis included 6,466 individuals followed for a median 8.8 years. The researchers found that baseline IL-6 levels were associated with a higher subsequent obesity-related cancer risk. A one standard deviation increase in log-transformed IL-6 was tied to a 19 percent increased risk for obesity-related cancers in an adjusted analysis (hazard ratio, 1.19; 95 percent confidence interval, 1.07 to 1.31). There was no strong association seen between TNF-α (hazard ratio, 1.08; 95 percent confidence interval, 0.98 to 1.19) or hsCRP (hazard ratio, 1.08; 95 percent confidence interval, 0.97 to 1.21) and obesity-related cancers. In 4,335 participants with available smoking habit information, similar associations were seen. The addition of IL-6 to a cancer prediction model conferred a small but statistically significant increase in the C-index, from 0.685 to 0.693, whereas the addition of hsCRP or TNF-α did not result in improvements in model performance. "Understanding which individuals are at higher risk for these cancers would allow for more targeted and effective monitoring and early detection, potentially improving outcomes through earlier intervention and personalized treatment," Bennetsen said in a statement. Abstract More Information Whatever your topic of interest, subscribe to our newsletters to get the best of Drugs.com in your inbox.

WEDNESDAY, Aug. 7, 2024 -- For individuals newly diagnosed with type 2 diabetes mellitus (T2DM), both short and long sleep duration are associated with microvascular disease, according to a study scheduled to be presented at the annual meeting of the European Association for the Study of Diabetes, being held from Sept. 9 to 13 in Madrid. Mette S. Johansen, from Odense University Hospital in Denmark, and colleagues explored the relationship between sleep duration and the presence of microvascular disease among individuals newly diagnosed with T2DM in a cross-sectional analysis. Sleep duration at night was measured using accelerometers worn for a period of 10 days and was categorized into short (less than seven hours), optimal (seven to less than nine hours), and long (at least nine hours). Overall, 396 participants had valid sleep duration measures, as well as urine albumin/creatinine ratio measurement and eye examination (used to define microvascular disease). The researchers found that 12, 60, and 28 percent of the participants had short, optimal, and long sleep duration, respectively. Within these categories, the prevalence of microvascular damage was 38, 18, and 31 percent, respectively. Both short and long sleep duration were significantly associated with microvascular disease (odds ratios, 2.63 and 2.29, respectively). Age accentuated the association between short sleep duration and microvascular disease for participants aged younger than 62 years compared with those aged older than 62 years. "Age amplifies the association between short sleep duration and microvascular disease, suggesting increased vulnerability among older individuals," the authors write. More Information Whatever your topic of interest, subscribe to our newsletters to get the best of Drugs.com in your inbox.