Weekend Links

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photo courtesy of Perimeter Institute 

We’ve hand-picked a mix of Massachusetts General Hospital and other research-related news and stories for your weekend reading enjoyment:

Vanishing Bone: The Medical Mystery That Could Have Derailed Millions Of Hip Replacements – The twist-filled backstory of disaster averted as told by former Mass General chief of joint replacement surgery Dr. William H. Harris

Standard Age For Mammograms Puts Nonwhite Women At Risk, Study Finds – New research from Mass General’s Dr. David Chang finds that current guidelines recommending mammograms beginning at age 50 were developed based on the majority population and may not be applicable to minority women. The results highlight the need to respect racial differences at the scientific research stage in order to improve delivery of clinical care.

Pediatricians Call For Universal Depression Screening For Teens – The American Academy of Pediatrics recently issued updated guidelines that call for universal screening for depression. How do experts think this will help the growing mental health problem among teens?

Forces of nature: great women who changed science – Celebrating the many fundamental discoveries made by women who pursued their research in the face of gender discrimination and did not get the recognition they deserved (plus a free collection of print-at-home posters of these remarkable women!)

Scientists show how the brain may be wired for drinking fluids – How do our bodies know when we’ve quenched our thirst and have had enough water? An NIH-funded study of mice provides a detailed diagram of the brain circuits behind thirst and satiety.

These scientific images are both research tools and works of art – In acknowledgement of science’s visual underpinnings, the Massachusetts Institute of Technology’s Koch Institute has for the past eight years featured a public gallery of science images in its Cambridge, Mass., lobby. Here’s a look at this year’s 10 winners.

Researchers Use Machine Learning to Improve Breast Cancer Screening Techniques

Imagine enduring a painful, expensive and scar-inducing surgery—only to find out afterwards that it wasn’t necessary.

This is the situation for many women with 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. Following surgical removal, 90% of these lesions end up being benign.

A change in the standard of care could be on the horizon thanks to researchers at Massachusetts General Hospital and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) who have found a more precise and less invasive way to separate harmful lesions from benign ones.

From left: Manisha Bahl, director of the Massachusetts General Hospital Breast Imaging Fellowship Program; MIT Professor Regina Barzilay; and Constance Lehman, chief of the Breast Imaging Division at MGH’s Department of Radiology. Photo courtesy of MIT News

“The decision about whether or not to proceed to surgery is challenging, and the tendency is to aggressively treat these lesions [and remove them],” said Manisha Bahl, MD, Director of the Breast Imaging Fellowship Program at Mass General, in a recent interview.

Bahl, along with a team of researchers, 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. They created this model by feeding it data on over 600 high-risk lesions, including information on the patient’s demographics and pathology reports, and then tasked it to identify patterns among the different data elements.

Through a process called deep learning, the machine uses the data to create an algorithm that can be used to predict which high-risk lesions should be surgically removed. This process differs from traditional software programming in that the researchers did not give the machine the formula for diagnosis, but rather let it analyze the data and identify patterns on its own.

“To our knowledge, this is the first study to apply machine learning to the task of distinguishing high-risk lesions that need surgery from those that don’t,” said collaborator Constance Lehman, MD, PhD, chief of the Breast Imaging Division at Mass General’s Department of Radiology, in a recent interview. “We believe this could support women to make more informed decisions about their treatment and that we could provide more targeted approaches to health care in general.”

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

Mass. General Study Identifies Genomic Differences Between Breast Cancers of African American and White Women

“In addition to having a higher prevalence of triple-negative breast cancers than Caucasian women – something that has been documented in previous studies – we found that African American women with breast cancer had a significantly higher prevalence of the TP53 driver mutation, basal tumor subtype and greater genomic diversity within tumors, all of which suggest more aggressive tumor biology,” says Tanya Keenan, MD, of the MGH Cancer Center, lead author of the study. “The higher risk of tumor recurrence that we observed among African American women was reduced when controlling for those factors, suggesting that these genomic differences contribute, at least partly, to the known racial disparity in the survival of African American and Caucasian breast cancer patients.”