Artificial Intelligence Accurately Predicts Breast Cancer Years Before Diagnosis

For decades, various strategies have been employed to enhance the interpretive performance of mammography, including double reading. Recently, several commercial artificial intelligence (AI) algorithms have received regulatory approval as supplementary tools for radiologists, showing promising results in detecting cancer on mammograms. These AI algorithms are designed to highlight areas of concern and provide breast-level and examination-level malignant neoplasm scores to assist interpreting radiologists. However, emerging research indicates that these same AI scores may also identify imaging features linked to future breast cancer years before they are clinically diagnosed. If commercial AI algorithms developed for immediate cancer detection can also assess future cancer risk, then more accurate and reliable short-term risk estimation could facilitate personalized preventive measures (e.g., more frequent or supplemental imaging), potentially leading to earlier breast cancer detection and less aggressive treatment. Analyses of AI breast cancer detection scores from consecutive mammography screenings prior to diagnoses are essential to evaluate the potential of these tools for estimating future disease risk. A new study has now investigated whether a commercial AI algorithm for breast cancer detection could predict the development of future cancer.

This collaborative study involving researchers from the Norwegian Institute of Public Health (NIPH, Oslo, Norway) utilized AI cancer detection scores recorded during multiple consecutive screening rounds of the national screening program, BreastScreen Norway. The researchers combined consecutive AI scores with long-term cancer outcomes to determine whether Lunit Inc.’s (Seoul, South Korea) INSIGHT MMG, a regulatory-cleared commercial AI algorithm for breast cancer detection, could estimate the onset of future breast cancers identified in subsequent screening rounds. Lunit INSIGHT MMG analyzes mammography images with 97% accuracy, pinpointing lesions that are suspicious for breast cancer and providing an abnormality score reflecting the likelihood of the existence of detected lesions.

This retrospective cohort study included 116,495 women aged 50 to 69 years who had no prior history of breast cancer and underwent at least three consecutive biennial screening examinations. The researchers employed scores from INSIGHT MMG for breast cancer detection and gathered screening data from multiple consecutive rounds of mammography. The study findings, published in JAMA, indicate that INSIGHT MMG could signal breast cancer up to six years before it develops. The AI system’s discriminatory accuracy for predicting future screening-detected or interval cancer risk 4 to 6 years before diagnosis met or exceeded the performance of established risk calculators currently in widespread use. These results suggest that commercial AI algorithms could help identify women at high risk of developing future breast cancer, paving the way for personalized screening strategies to facilitate earlier diagnosis.

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NIPH
Lunit Inc.