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Machine Learning Method Can Help Women Avoid Unnecessary Breast Surgery

By Medimaging International staff writers
Posted on 19 Mar 2019
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Image: The new machine learning method is designed to predict atypical ductal hyperplasia (Photo courtesy of iStock).
Image: The new machine learning method is designed to predict atypical ductal hyperplasia (Photo courtesy of iStock).
A team of researchers from the Geisel School of Medicine at Dartmouth (Hanover, NH, USA) has developed a machine learning method to predict atypical ductal hyperplasia (ADH) upgrade to cancer.

ADH, a breast lesion associated with a four- to five-fold increase in the risk of breast cancer, is mainly found using mammography and identified on core needle biopsy. Despite multiple passes of the lesion during biopsy, only portions of the lesions are sampled. Other variable factors influence sampling and accuracy such that the presence of cancer may be underestimated by 10-45%. Currently, surgical removal is recommended for all ADH cases found on core needle biopsies to determine if the lesion is cancerous. About 20-30% of ADH cases are upgraded to cancer after surgical excision. However, this means that 70-80% of women undergo a costly and invasive surgical procedure for a benign (but high-risk) lesion.

The new machine learning method to predict ADH upgrade to cancer can potentially help clinicians and low-risk patients decide whether active surveillance and hormonal therapy is a reasonable alternative to surgical excision. An evaluation of the model by the researchers showed that the machine learning approach can identify 98% of all malignant cases prior to surgery while sparing from surgery 16% of women who otherwise would have undergone an unnecessary operation for a benign lesion. The researchers now plan to expand the scope of their model by including other high-risk breast lesions such as lobular neoplasia, papillomas, and radial scars. They also plan on further validating their approach on large external datasets using state and national breast cancer registries, and collaborating with other medical centers.

"Our results suggest there are robust clinical differences between women at low versus high risk for ADH upgrade to cancer based on core needle biopsy data that allowed our machine learning model to reliably predict malignancy upgrades in our dataset," said Saeed Hassanpour, PhD, who led the Dartmouth research team. "This study also identified important clinical variables involved in ADH upgrade risk."

"Our model can potentially help patients and clinicians choose an alternative management approach in low-risk cases," added Hassanpour. "In the era of personalized medicine, such models can be desirable for patients who value a shared decision-making approach with the ability to choose between surgical excision for certainty versus surveillance to avoid cost, stress, and potential side effects in women at low risk for upgrade of ADH to cancer."

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Geisel School of Medicine at Dartmouth

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