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New way to detect breast cancer earlier

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ScreenPoint Medical’s Transpara AI decision support system can help radiologists to identify potential breast cancers earlier and faster, according to a new study published in Radiology. The ground breaking evidence-based software is already in clinical use in over 30 countries including the USA, France, Germany and Spain.  

The incidence of breast cancer is increasing worldwide due to environmental, diet and lifestyle changes but increasingly, countries are reporting a shortage of specialist breast radiologists. In the UK and other countries, every mammogram is read by two specialist radiologists. However, this is expensive and elsewhere often radiologists work alone. In the US for example, 60% of radiologists reading mammograms are general radiologists.

It is known that overall, up to 25% of breast cancers are missed by screening and considered detectable in retrospect. The earlier a cancer is discovered, the earlier a patient can be treated and the greater the chance of surviving the disease.

This new study investigated over 2,000 interval cancers which were missed at the time of screening. Transpara was able to independently identify up to 37.5% of these exams.

Professor Nico Karssemeijer, ScreenPoint Medical’s CEO, said: “We are fortunate to be working with leading clinicians in the field to investigate breast AI and understand its strengths and limitations. We are committed to support studies that provide clinical evidence so that we can safely introduce our technology. This large study confirms the potential of AI to improve early detection of subtle cancers. This is a real game changer and shows that radiologists who work with AI can improve patient care significantly.”

Professor Carla van Gils of University Medical Center, who led the DENSE trial in the Netherlands and who is one of the authors of the paper, added: “In this study, adding AI to breast density measurement led to a significant improvement in determining risk of an interval cancer. The combination of methods can help us to pinpoint the group of breast screening participants who will benefit most from supplemental MRI screening, in terms of reducing interval cancers.”

The study found that by combining Transpara breast care with breast density, which is a well known risk factor, it was possible to flag up to 51% of women diagnosed with cancer in the interval after a negative screening. This is a major step towards using Transpara AI for image-based short term risk measurement.

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About the author


Editor in chief for eTurboNew is Linda Hohnholz. She is based in the eTN HQ in Honolulu, Hawaii.

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