{"id":26650,"date":"2020-12-29T23:35:37","date_gmt":"2020-12-29T23:35:37","guid":{"rendered":"http:\/\/cmshub.wpengine.com\/?p=330764"},"modified":"2024-11-03T15:15:50","modified_gmt":"2024-11-03T15:15:50","slug":"researchers-find-new-effective-tools-to-determine-breast-cancer-risk-using-mammograms","status":"publish","type":"post","link":"https:\/\/ogkologos.com\/en\/researchers-find-new-effective-tools-to-determine-breast-cancer-risk-using-mammograms\/","title":{"rendered":"Researchers Find New, Effective Tools to Determine Breast Cancer Risk Using Mammograms"},"content":{"rendered":"

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Mammograms are an important tool for detecting possible breast cancer, but researchers in Australia have found new ways to use mammograms to predict future risk for breast cancer, as well.<\/span><\/p>\n

In a new study published<\/a> in the International Journal of Cancer<\/em>, a University of Melbourne-led effort discovered two new methods to predict risk with digital mammography. When used together, they were found to be better at letting women know their risk than breast density, genetic mutations, and other risk factors. Researchers say this could mean improved screening methods, decreased mortality rates, and less stress for women.<\/span><\/p>\n

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PHOTO: ADOBE STOCK\/HERO IMAGES<\/a><\/span><\/figcaption><\/figure>\n

Lead researcher, University of Melbourne professor John Hopper, says<\/a>, \u201cThese measures could revolutionize mammographic screening at little extra cost, as they simply use computer programs. The new measures could also be combined with other risk factors collected at screening, such as family history and lifestyle factors, to provide an even stronger and holistic picture of a woman\u2019s risk.<\/span><\/p>\n

\u201cTailored screening \u2014 not \u2018one size fits all\u2019 \u2014 could then be based on accurately identifying women at high, as well as low, risk so that their screening can be personalized.\u201d<\/span><\/p>\n

He added that this is important because it could give women recommendations based on their own personal risk of developing the disease, not just based on their age.<\/span><\/p>\n

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University of Melbourne researchers teamed up with Cancer Council Victoria and BreastScreen Victoria on the study. They used computer programs to<\/a> analyze mammography images from women with and without breast cancer, which led them to find two new ways to determine risk: Cirrocumulus, based on the brightest areas of the images, and Cirrus, based on texture.<\/span><\/p>\n

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PHOTO: ADOBE STOCK\/WAVEBREAKMEDIAMICRO<\/a><\/span><\/figcaption><\/figure>\n

A semi-automated computer method was used to measure density at the typical and progressively higher levels of brightness to create Cirrocumulus. To create Cirrus, they used artificial intelligence and high-speed computing to learn about new aspects of the texture of a mammogram that predict breast cancer risk.<\/span><\/p>\n

Researchers say when both were used, they were significantly better at risk prediction than any other known risk factors. Hopper says this could give more women confidence in the screening process.<\/span><\/p>\n

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He explains<\/a>, \u201cOnly around 55 per cent of Australian women aged 50-74 currently present for screening aimed at detecting breast cancers early.<\/span><\/p>\n

\u201cKnowing that screening could also give an accurate risk prediction could encourage more women to take up the offer of free screening. Women with high risk based on their mammogram would also benefit greatly from also knowing their genetic risk.\u201d<\/span><\/p>\n

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PHOTO: PIXABAY\/ALARCONAUDIOVISUAL<\/a><\/span><\/figcaption><\/figure>\n

Adjunct Associate Professor Helen Frazer serves as Clinical Director of St. Vincent\u2019s BreastScreen Melbourne. She feels this development could have huge impacts on screening as we know it, as well as getting the upper hand against the disease.<\/span><\/p>\n

She says<\/a>, \u201cUsing AI developments to assess risk and personalize screening could deliver significant gains in the fight against breast cancer.\u201d<\/span><\/p>\n

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