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AI-Based Breast Cancer Risk Calculator Could Reduce Unnecessary Biopsies

By MedImaging International staff writers
Posted on 17 Aug 2023
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Image: A new tool leverages artificial intelligence for breast cancer diagnosis (Photo courtesy of Freepik)
Image: A new tool leverages artificial intelligence for breast cancer diagnosis (Photo courtesy of Freepik)

Breast cancer is a significant health concern, affecting one in eight women during their lifetime and even some men. Mammograms have become a standard screening procedure, recommended annually for women starting at age 40 to detect breast cancer at its earliest stages. Although this leads to more biopsies, a surprisingly small proportion (less than two out of 10,000 women biopsied) comes out positive. The consequences are not only wasted time, resources, and money but also unnecessary anxiety for the patient. Now, scientists have devised a more intelligent model to assess breast cancer risk, aiming to reduce unnecessary biopsies.

Scientists at Houston Methodist Hospital (Houston, TX, USA) have developed an advanced clinical decision support tool, known as iBRISK (intelligent-augmented breast cancer risk calculator), which leverages deep learning to provide a more accurate assessment of a woman's risk of developing breast cancer. This tool was created by applying deep learning to clinical risk factors and mammographic descriptors from nearly 10,000 individuals, and its effectiveness was subsequently validated on more than 1,000 additional patients.

Currently, hospitals in the U.S. rely on the Breast Imaging Reporting and Database System (BI-RADS), devised by the American College of Radiology, to gauge breast cancer risk and determine whether a biopsy is needed. However, the scientists at Houston Methodist Hospital have gone beyond the standard BI-RADS data by utilizing AI technology and multiple patient data points to refine the assessment. The iBRISK system integrates natural language processing, medical image analysis, and deep learning with multi-modal BI-RADS patient data to generate one of three recommendations: a biopsy is not recommended, consideration for biopsy, or a biopsy is recommended. The researchers identified approximately 100 parameters for analysis, such as age, sex, socio-economic data, medical history, and insurance plans. Through the application of deep learning, the AI tool reduced these data points down to the 20 most essential risk indicators.

In a new study, the iBRISK model was applied to an independent set of breast images from over 4,200 patients, screened at three different institutions between 2006 and 2016. The model was developed particularly to evaluate the probability of malignancy of BI-RADS category 4 lesions. The model's accuracy rate was found to be approximately 89.5%, with an impressive specificity of 81%. Only two of the 1,228 individuals in the low Probability of Malignancy (POM) group had malignant lesions, while the high POM malignancy rate was 85.9%. The scientists also calculated the iBRISK score's effectiveness at predicting malignancy, resulting in an area under the receiver operating characteristic curve of 0.97. The potential impact of iBRISK is substantial. Not only could it lead to more accurate breast cancer risk assessments, but it may also save hundreds of millions of dollars each year at a single institution by eliminating unnecessary biopsies. By employing intelligent AI methodologies, the iBRISK tool represents a significant advancement in breast cancer screening and demonstrates the potential to improve medical decision-making, efficiency, and patient care.

“Our study demonstrates that iBRISK can effectively aid in risk stratification of BI-RADS 4 lesions and reduce overbiopsy of these lesions,” the authors concluded. “Ultimately, the iBRISK calculator will be published as an online interface and made open access, noncommercial, and accessible by health systems and centers worldwide. Future studies aim to improve the model further, particularly by including more granular data and other BI-RADS categories.”

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Houston Methodist Hospital 

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