Aidence, RadNet’s AI subsidiary, enters collaboration with Google Health to improve lung cancer
Press releases may be edited for formatting or style | November 29, 2022
Health IT
LOS ANGELES, Nov. 28, 2022 (GLOBE NEWSWIRE) -- RadNet, Inc. (NASDAQ: RDNT), a national leader in providing high-quality, cost-effective, fixed-site outpatient diagnostic imaging services today reported that its lung artificial intelligence subsidiary, Aidence, and Google Health, a division of Alphabet, Inc. (NASDAQ: GOOG), announce an agreement to license Google Health’s AI research model for lung nodule malignancy prediction on CT imaging. Aidence will develop, validate and bring this model to the market to support the early and accurate diagnosis of lung cancer and the reduction of unnecessary procedures in screening programs.
Lung cancer screening with low-dose CT has been shown to significantly reduce lung cancer mortality by as high as 24% for men and 33% for women, according to the 2020 NELSON trial. Screening initiatives are increasingly being implemented in Europe, such as the UK’s Targeted Lung Health Checks. In the United States, eligibility criteria have recently been broadened, further reflecting the benefit of lung cancer screening.
A major difficulty in lung cancer screening is establishing the nature of detected lung nodules. Most of these nodules are not cancerous. However, properly identifying and diagnosing such nodules can be time-consuming, costly, anxiety-inducing for patients and their families and sometimes invasive, requiring follow-up CTs or surgical interventions.
Dr Raymond Osarogiagbon, Chief Scientist, Baptist Memorial Health Care Corporation and Director, Multidisciplinary Thoracic Oncology Program, Baptist Cancer Center, Memphis, Tennessee, explained, “One of the most exciting developments in contemporary population healthcare is the early detection of lung cancer. Unfortunately, the reality that most such nodules will be benign represents a real challenge that cries out for a technological solution. Artificial intelligence is one such solution.”
Dr Osarogiagbon continued, “The world looks forward to the rapid development and validation of software that will enhance our ability to find the many lung cancer needles in the giant haystack that is CT-detected lung nodules in today’s clinical practice.”
Deep learning, a subset of AI, has been shown to support the risk scoring of lung nodule malignancy. In a study published in Nature in 2018, scientists affiliated with Google Health presented a highly accurate model for malignancy classification, consistently matching the performance of experienced radiologists.
Lung cancer screening with low-dose CT has been shown to significantly reduce lung cancer mortality by as high as 24% for men and 33% for women, according to the 2020 NELSON trial. Screening initiatives are increasingly being implemented in Europe, such as the UK’s Targeted Lung Health Checks. In the United States, eligibility criteria have recently been broadened, further reflecting the benefit of lung cancer screening.
A major difficulty in lung cancer screening is establishing the nature of detected lung nodules. Most of these nodules are not cancerous. However, properly identifying and diagnosing such nodules can be time-consuming, costly, anxiety-inducing for patients and their families and sometimes invasive, requiring follow-up CTs or surgical interventions.
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Dr Osarogiagbon continued, “The world looks forward to the rapid development and validation of software that will enhance our ability to find the many lung cancer needles in the giant haystack that is CT-detected lung nodules in today’s clinical practice.”
Deep learning, a subset of AI, has been shown to support the risk scoring of lung nodule malignancy. In a study published in Nature in 2018, scientists affiliated with Google Health presented a highly accurate model for malignancy classification, consistently matching the performance of experienced radiologists.
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