2023-08-17 10:00:18
Title: Artificial Intelligence Identifies New Indicator for Breast Cancer Development in Women with Dense Breast Tissue
Subtitle: UMC Utrecht Study Highlights Potential for Enhanced MRI Screening
Date: [current date]
Author: [author’s name]
Institution: [publishing institution]
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UMC Utrecht researchers have made a groundbreaking discovery using artificial intelligence (AI) to identify a new factor that may aid in the early detection of breast cancer in women with dense breast tissue. The study reveals that the extent to which normal glandular and connective tissue lights up during an MRI with the contrast agent may serve as an additional indication of cancer development.
Every two years, Dutch women between the ages of 50 and 75 receive a call for breast cancer screening. While a mammogram is commonly used for early detection, approximately 8 percent of women with dense breasts find it challenging to identify tumors due to the predominance of mammary glands and connective tissue. These women have a three to six times higher risk of developing breast cancer compared to those with more fatty tissue.
Background parenchymal enhancement (BPE) is another lesser-known risk factor that plays a role in breast cancer development. During an MRI scan, a contrast fluid is injected to highlight the tumor against healthy tissue. However, ‘normal’ glandular tissue also lights up due to the presence of the fluid. The AI computer model developed by PhD student Hui Wang analyzed MRI scans of 4,553 women with dense breasts, highlighting various additional risk factors.
The study aimed to determine when women with dense breasts are most at risk of developing breast cancer, and the results showed that an increased BPE can serve as an additional indication for cancer development. This groundbreaking finding has been published in the leading journal Radiology.
For the first time, BPE has been linked specifically to the development of cancer in women with very dense breast tissue. The use of AI allowed the computer model to process all interpretations of elevated BPE simultaneously, eliminating the need for a specific definition for the term.
The study made use of an extensive amount of MRI scans obtained from the DENSE trial, which followed approximately 5,000 women with dense breasts between 2011 and 2016. The results demonstrated that MRI scans, in addition to mammograms, were effective in detecting cancer in women with very dense breasts and proved to be cost-effective.
However, it is worth noting that an additional MRI scan can have potential disadvantages, including the possibility of false positive results leading to stress, unnecessary tests, and costly treatments. Capacity issues may also arise due to the increased number of MRI scans required. As a result, the Ministry of Health, Welfare and Sport is exploring alternative options, such as contrast mammography.
Nevertheless, the new AI study has the potential to optimize the use of MRI in population screening by saving time and resources. By incorporating AI in the assessment of MRI scans, a more targeted approach can be taken, reducing the need for personnel and equipment. The study also paves the way for personalized frequency scans based on individual risk factors found during the initial MRI.
UMC Utrecht’s collaboration within Oncomid for cancer care and research positions the institution at the forefront of oncological innovations. These include advancements in image-driven techniques, such as MRI, AI, and organoids. With the goal of sustainable, affordable, and efficient care, UMC Utrecht continues to drive innovations suitable for the future.
Source: UMC Utrecht
Tags: Breast Cancer, Dense Breast Tissue, Artificial Intelligence, MRI Screening, UMC Utrecht, Cancer Research, Early Detection, Women’s Health]
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