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Researchers developed a deep learning model that identifies imaging biomarkers on screening mammograms to predict a patient’s risk of developing breast cancer more accurately than traditional risk assessment tools.
Traditional risk assessment models do not take advantage of the level of detail contained in a mammogram, ”said study author Leslie Lamb of Massachusetts General Hospital (MGH) in the United States.
“Even the best existing traditional risk models can separate patient subgroups but are not as accurate on an individual level,” Lamb added.
Currently, available risk assessment models incorporate only a small portion of patient data such as family history, previous breast biopsies, and hormonal and reproductive history.
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Only one feature of the screening mammogram itself, breast density, is incorporated into traditional models.
The research team developed a new deep learning algorithm to predict breast cancer risk using data from five MGH breast cancer screening sites.
The model was developed on a population that included women with a personal history of previous breast cancer, implants or biopsies.
The study included 245,753 consecutive 2D digital bilateral screening mammograms performed on 80,818 patients between 2009 and 2016.
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Of the total mammograms, 210,819 exams in 56,831 patients were used for training, 25,644 exams from 7,021 patients for testing and 9,290 exams from 3,961 patients for validation.
Using statistical analysis, the researchers compared the accuracy of the deep learning image-only model with a commercially available risk assessment model (Tyrer-Cuzick version 8) to predict future breast cancer within five years of mammography. index.
The deep learning model achieved a predictive rate of 0.71, significantly exceeding the traditional risk model, which achieved a rate of 0.61.
“Our deep learning model is capable of translating the full diversity of fine imaging biomarkers into mammography that can predict a woman’s future risk for breast cancer,” Lamb said.
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The study is expected to be presented at the annual meeting of the Radiological Society of North America (RSNA) from November 29 to December 5. (IANS)
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