Breast cancer survivors rally in a support group.
Every two minutes, a woman is diagnosed with breast cancer. The question is, can those at high risk be identified early enough to survive?
New AI software has predicted risk equally well in both white and black women for the first time.
The current standard practice in medicine is not exactly precise. It relies on age, family history of cancer, and breast density, among other factors, to determine risk. But these factors do not always tell the whole story, leaving many women to slip through the cracks. In addition, a racial gap persists in breast cancer treatment and survival. African-American women are 42 percent more likely to die from the disease despite relatively equal rates of diagnosis.
But now those grim statistics could be changing. A team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory have developed a deep learning model that can more accurately predict a patient's breast cancer risk compared to established clinical guidelines – and it has predicted risk equally well in both white and black women for the first time.
The Lowdown
Study results published in Radiology described howthe AI software read mammogram images from more than 60,000 patients at Massachusetts General Hospital to identify subtle differences in breast tissue that pointed to potential risk factors, even in their earliest stages. The team accessed the patients' actual diagnoses and determined that the AI model was able to correctly place 31 percent of all cancer patients in the highest-risk category of developing breast cancer within five years of the examination, compared to just 18 percent for existing models.
"Each image has hundreds of thousands of pixels identifying something that may not necessarily be detected by the human eye," said MIT professor Regina Barzilay, one of the study's lead authors. "We all have limited visual capacities so it seems some machines trained on hundreds of thousands of images with a known outcome can capture correlations the human eye might not notice."
Barzilay, a breast cancer survivor herself, had abnormal tissue patterns on mammograms in 2012 and 2013, but wasn't diagnosed until after a 2014 image reading, illustrating the limitations of human processing alone.
MIT professor Regina Barzilay, a lead author on the new study and a breast cancer survivor herself.
(Courtesy MIT)
Next up: The MIT team is looking at training the model to detect other cancers and health risks. Barzilay recalls how a cardiologist told her during a conference that women with heart diseases had a different pattern of calcification on their mammograms, demonstrating how already existing images can be used to extract other pieces of information about a person's health status.
Integration of the AI model in standard care could help doctors better tailor screening and prevention programs based on actual instead of perceived risk. Patients who might register as higher risk by current guidelines could be identified as lower risk, helping resolve conflicting opinions about how early and how often women should receive mammograms.
Open Questions: While the results were promising, it's unknown how well the model will work on a larger scale, as the study looked at data from just one institution and used mammograms supplied by just one hospital. Some risk factor information was also unavailable for certain patients during the study, leaving researchers unable to fully compare the AI model's performance to that of the traditional standard.
One incentive to wider implementation and study, however, is the bonus that no new hardware is required to use the AI model. With other institutions now showing interest, this software could lead to earlier routine detection and treatment of breast cancer — resulting in more lives saved.
Thanks to safety cautions from the COVID-19 pandemic, a strain of influenza has been completely eliminated.
The flu shot, explained
Influenza viruses type A and B are responsible for the majority of human illnesses and the flu season.
Centers for Disease Control
For more than a decade, flu shots have protected against two types of the influenza virus–type A and type B. While there are four different strains of influenza in existence (A, B, C, and D), only strains A, B, and C are capable of infecting humans, and only A and B cause pandemics. In other words, if you catch the flu during flu season, you’re most likely sick with flu type A or B.
Flu vaccines contain inactivated—or dead—influenza virus. These inactivated viruses can’t cause sickness in humans, but when administered as part of a vaccine, they teach a person’s immune system to recognize and kill those viruses when they’re encountered in the wild.
Each spring, a panel of experts gives a recommendation to the US Food and Drug Administration on which strains of each flu type to include in that year’s flu vaccine, depending on what surveillance data says is circulating and what they believe is likely to cause the most illness during the upcoming flu season. For the past decade, Americans have had access to vaccines that provide protection against two strains of influenza A and two lineages of influenza B, known as the Victoria lineage and the Yamagata lineage. But this year, the seasonal flu shot won’t include the Yamagata strain, because the Yamagata strain is no longer circulating among humans.
How Yamagata Disappeared
Flu surveillance data from the Global Initiative on Sharing All Influenza Data (GISAID) shows that the Yamagata lineage of flu type B has not been sequenced since April 2020.
Nature
Experts believe that the Yamagata lineage had already been in decline before the pandemic hit, likely because the strain was naturally less capable of infecting large numbers of people compared to the other strains. When the COVID-19 pandemic hit, the resulting safety precautions such as social distancing, isolating, hand-washing, and masking were enough to drive the virus into extinction completely.
Because the strain hasn’t been circulating since 2020, the FDA elected to remove the Yamagata strain from the seasonal flu vaccine. This will mark the first time since 2012 that the annual flu shot will be trivalent (three-component) rather than quadrivalent (four-component).
Should I still get the flu shot?
The flu shot will protect against fewer strains this year—but that doesn’t mean we should skip it. Influenza places a substantial health burden on the United States every year, responsible for hundreds of thousands of hospitalizations and tens of thousands of deaths. The flu shot has been shown to prevent millions of illnesses each year (more than six million during the 2022-2023 season). And while it’s still possible to catch the flu after getting the flu shot, studies show that people are far less likely to be hospitalized or die when they’re vaccinated.
Another unexpected benefit of dropping the Yamagata strain from the seasonal vaccine? This will possibly make production of the flu vaccine faster, and enable manufacturers to make more vaccines, helping countries who have a flu vaccine shortage and potentially saving millions more lives.
Founder Lewis Hornby and his grandmother Pat, sampling Jelly Drops—an edible gummy containing water and life-saving electrolytes.
When Lewis Hornby visited his grandmother at her nursing home afterward, he learned that dehydration especially affects people with dementia, as they often don’t feel thirst cues at all, or may not recognize how to use cups correctly. But while dementia patients often don’t remember to drink water, it seemed to Hornby that they had less problem remembering to eat, particularly candy.
Where people with dementia often forget to drink water, they're more likely to pick up a colorful snack, Hornby found. alzheimers.org.uk
Hornby wanted to create a solution for elderly people who struggled keeping their fluid intake up. He spent the next eighteen months researching and designing a solution and securing funding for his project. In 2019, Hornby won a sizable grant from the Alzheimer’s Society, a UK-based care and research charity for people with dementia and their caregivers. Together, through the charity’s Accelerator Program, they created a bite-sized, sugar-free, edible jelly drop that looked and tasted like candy. The candy, called Jelly Drops, contained 95% water and electrolytes—important minerals that are often lost during dehydration. The final product launched in 2020—and was an immediate success. The drops were able to provide extra hydration to the elderly, as well as help keep dementia patients safe, since dehydration commonly leads to confusion, hospitalization, and sometimes even death.
Not only did Jelly Drops quickly become a favorite snack among dementia patients in the UK, but they were able to provide an additional boost of hydration to hospital workers during the pandemic. In NHS coronavirus hospital wards, patients infected with the virus were regularly given Jelly Drops to keep their fluid levels normal—and staff members snacked on them as well, since long shifts and personal protective equipment (PPE) they were required to wear often left them feeling parched.
In April 2022, Jelly Drops launched in the United States. The company continues to donate 1% of its profits to help fund Alzheimer’s research.