A woman checking her breast for the presence of concerning lumps.
Nancy Cappello was proactive. When she turned 36, she had a baseline mammogram, a standard medical recommendation in the late 1980s and early 1990s as a comparison tool for future screenings. At 40, Cappello started getting them annually.
Her breast surgeon estimated the cancer had been festering for four to five years under the radar of her annual mammograms.
Six weeks after her 11th-consecutive normal mammogram, she was diagnosed with Stage IIIc breast cancer.
A doctor felt a lump while doing a breast exam during her annual physical and a subsequent ultrasound detected cancer that had spread to 13 lymph nodes. That's when Cappello, then 51, learned she had dense breast tissue, making mammography less likely to detect tumors in her breasts.
She also discovered through her own research that she was among the 40 to 50 percent of women with dense breast tissue — almost half the female population — but medical protocol did not require physicians to inform women of their dense tissue status. If she had known, she said, she would have gotten an ultrasound every year in addition to a mammogram that could have detected the cancer much earlier. Cappello said her breast surgeon estimated the cancer had been festering for four to five years under the radar of her annual mammograms.
Although ultrasound as a cancer screening tool has been available for decades, technological advances are helping doctors find more invasive cancers in women with dense breasts, in turn giving women who know their tissue status the opportunity for earlier detection and treatment.
"We know that the gold standard for breast cancer screening is mammography, but in women with dense breast tissue, up to one third of breast cancers can be missed with this modality alone."
Dr. Georgia Giakoumis Spear, chief of the department of breast imaging at NorthShore University HealthSystem in suburban Chicago and assistant professor of radiology at the University of Chicago, has been a leader in developing standards for the use of new ultrasound technology. She is leading a study to develop more specific national guidelines around the use of Automated Whole Breast Tissue Ultrasound (ABUS), a non-invasive procedure in which sound waves are used to scan breast tissue while a patient lies on her back with her arm over her head.
Approved by the Food and Drug Administration in 2012, ABUS provides higher quality 3D images and faster delivery to provide more accurate results than past ultrasound technology. The scan does not involve radiation, and a practitioner can complete the process in 15 to 20 minutes, from patient preparation to image creation. NorthShore has been using ABUS since 2015, Dr. Spear said, and the technology can improve breast cancer detection in women with dense breasts by up to 55 percent.
"We know that the gold standard for breast cancer screening is mammography, but in women with dense breast tissue, up to one third of breast cancers can be missed with this modality alone," Spear says. "And when we supplement screening with ultrasound in this population of women, we have found a large number of cancers by ultrasound that are not visible on the mammogram."
Mammography should still be used as the first step for breast cancer detection, but if an initial mammogram shows that a patient has dense breast tissue, studies encourage discussion of additional screening with ultrasound.
On a mammogram, dense tissue appears white. So do cancerous masses, making them easy to miss.
A radiologist determines tissue density, according to the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). "A" and "B" breast density categories designate ratios of mostly fatty, or non-dense tissues, while the "C" and "D" categories designate heterogeneously dense and extremely dense tissue, respectively. Such patients would be classified as having dense tissue. Younger women, women with lower levels of body fat and women undergoing hormone therapy are more likely to have C and D breast density.
On a mammogram, dense tissue appears white. So do cancerous masses, making them easy to miss. Fatty tissue, in comparison, appears black, making tumors easier to spot.
The FDA stated among its policy goals for 2018 that it's placing an improved focus on recognizing technological advances to help "ensure women get the most relevant, up-to-date information about their breast density, which is now recognized as a risk factor for breast cancer." An article in the March 2018 Journal of the American College of Radiology recommended supplemental screening for women with higher-than-average breast cancer risk, placing women with dense breast tissue in that category.
To be sure, some in the medical community are reluctant to push for ultrasounds, saying that a mammogram might be enough even if the woman has dense breast tissue. A patient is advised to discuss the option of ultrasound with her physician and they can decide from there.
Access to such information became political for Cappello after her diagnosis in 2004. She said that as she underwent six surgeries, a mastectomy, chemotherapy, radiation and hormone therapy, she asked doctors why they weren't required to inform women of their dense breast tissue status. Her dissatisfaction with their responses led to the formation of Are You Dense, Inc., an advocacy group aimed to inform women of their medical options while working to pass legislation mandating that women know their tissue status. Other legislation has focused on mandating insurance coverage for breast ultrasounds.
Nancy Cappello.
(Courtesy)
Cappello's work led Connecticut to become the first state to pass an information law in 2009, and 35 states now have similar requirements. Depending on the state, the law could mandate that certain language or information about breast density be included in the patient's mammogram results, or require physicians to tell women about dense tissue if their breast density falls in the BI-RADS categories C and D. Other states might require that patients be given general information about breast density and advice to discuss their options with a physician. (Note: There is a chart on Cappello's website that shows what laws exist – or don't – in each state.)
Through her site and social media, she's connected with other women who've lobbied for laws in their states, including Dr. Spear, who recently testified before legislative committees in Illinois as they considered companion bills. The Illinois legislation is expected to be signed into law this summer.
"There should be no excuses," Cappello says. "Women should have this information. There should be no concealing or hiding of her status."
AI was integral to creating Moderna's mRNA vaccine against COVID.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
