THURSDAY, OCTOBER 5, 2021 (HealthDay News )

Mammograms have decreased mortality rates by spotting breast cancers when they are small and easier to cure, although they are less useful for women with dense breasts.

However, a recent study discovered that these women, who are more likely to acquire breast cancer , could benefit from additional MRI screening. Additionally, the process is being sped up by new technology.

According to Dutch researchers, artificial intelligence can rapidly and reliably sort through MRIs to rule out breast cancer in the majority of patients who don’t have it, freeing radiologists to focus on the more complex situations.

Researchers taught artificial intelligence software in the Dense Tissue and Early Breast Neoplasm Screening (DENSE) study to identify between breasts with and without lesions.

According to lead author Erik Verburg of the University Medical Center Utrecht in the Netherlands, “the Thick study revealed that further MRI screening for women with particularly dense breasts was advantageous.” The DENSE experiment, on the other hand, verified that the great majority of tested women have no abnormal MRI results.

Compared to women with fatter tissue, those with exceptionally thick breasts have less sensitive Mammography . Compared to women with fatty breasts, women with highly thick breasts had a six times higher risk of having breast cancer . They run twice as much risk as the typical woman.

The MRIs of over 9,200 extraordinarily thick breasts was examined in the study. Of those, 838 had at least one growth, while more than 8,300 had none. 77 of those had cancer.

91% of the MRIs with lesions were identified by the model for a radiologist’s examination. According to the study, it rejected around 40% of the lesion-free MRIs without skipping any cancers .

The results were released on October 5 in the journal Radiology.

Verburg stated in a journal news release that “we demonstrated that it is safe to employ artificial intelligence to safely disregard breast screening MRIs without missing any malignant illness.” “The outcomes exceeded our expectations. A excellent place to start is at 40%. However, we still need to improve by 60%.

According to Verburg, this AI-based approach has the potential to considerably lessen the workload of radiologists.

According to Verburg, the method can be utilized to help radiologists read more efficiently overall. As a result, more time might be freed up to concentrate on the truly challenging breast MRI tests.

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SOURCE: News release from the Radiological Society of North America, October 5, 2021

Murez, Cara
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