Researchers Use Machine Learning to Improve Breast Cancer Screening Techniques

The current standard treatment for high-risk breast lesions—areas of tissue that appear suspicious on a mammogram and have abnormal but not cancerous cells when tested by needle biopsy— is surgical removal. However, after underdoing this painful procedure, many women learn their surgery was unnecessary as 90% of lesions end up being benign.

A change in the standard of care could be on the horizon thanks to a team of researchers at Massachusetts General Hospital and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). They have harnessed the power of artificial intelligence (AI) to develop a more accurate and less invasive screening method for high risk lesions.

When tested, the machine correctly diagnosed 97 percent of 335 high-risk breast lesions as malignant and reduced the number of benign surgeries by more than 30 percent compared to existing approaches. These results were recently published in Radiology.

The team developed an AI system that uses machine learning to distinguish between high-risk lesions that need to be surgically removed from those that should just be watched over time. This is the first study to apply machine learning to the task of distinguishing high-risk lesions.

Mass General radiologists will begin incorporating the model into their clinical practice over the next year.

Constance Lehman, MD, PhD, chief of the Breast Imaging Division, and Manisha Bahl, MD, Director of the Breast Imaging Fellowship Program were collaborators in this research.

Liquid Biopsies Give Clues on When and Why Cancer Treatments Lose their Efficacy

Many cancer patients eventually develop a resistance to their treatments. To help oncologists quickly and accurately identify early signs of treatment resistance, researchers have developed a new diagnostic tool called a liquid biopsy. Now a team from the Mass General Cancer Center is providing confirmatory data that may help to move liquid biopsies into clinical practice. These data were presented at the ESMO 19th World Congress on Gastrointestinal Cancer.

A liquid biopsy is a diagnostic test that detects circulating tumor DNA (ctDNA), which is genetic material released by dying tumor cells that flows through the bloodstream. ctDNA can provide early notice when a treatment is no longer working. It also offers a more complete picture of the genetic changes in tumor cells that are driving the resistance to treatment, which could guide new treatment courses.

To test out liquid biopsy’s efficacy, Mass General Cancer Center investigators followed nearly 40 patients with various forms of gastrointestinal cancers who had experienced initial success with targeted therapies, but then began to show signs of treatment resistance. Using liquid biopsies, researchers identified one or more mutations or mechanisms that contributed to treatment resistance in 31 of the 40 patients. Fourteen of these patients had multiple mutations that contributed to resistance.

“Identifying what specific mutations are responsible for treatment resistance is very important in helping clinicians choose what treatment path a patient should try next,” said study investigator Aparna Parikh, MD, from the Mass General Cancer Center.

Parikh says the next step is to study how best to use this new technology in daily practice.

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