A model of a human kidney.
Science's dream of creating perfect custom organs on demand as soon as a patient needs one is still a long way off. But tiny versions are already serving as useful research tools and stepping stones toward full-fledged replacements.
Although organoids cannot yet replace kidneys, they are invaluable tools for research.
The Lowdown
Australian researchers have grown hundreds of mini human kidneys in the past few years. Known as organoids, they function much like their full-grown counterparts, minus a few features due to a lack of blood supply.
Cultivated in a petri dish, these kidneys are still a shadow of their human counterparts. They grow no larger than one-sixth of an inch in diameter; fully developed organs are up to five inches in length. They contain no more than a few dozen nephrons, the kidney's individual blood-filtering unit, whereas a fully-grown kidney has about 1 million nephrons. And the dish variety live for just a few weeks.
An organoid kidney created by the Murdoch Children's Institute in Melbourne, Australia.
Photo Credit: Shahnaz Khan.
But Melissa Little, head of the kidney research laboratory at the Murdoch Children's Institute in Melbourne, says these organoids are invaluable tools for research. Although renal failure is rare in children, more than half of those who suffer from such a disorder inherited it.
The mini kidneys enable scientists to better understand the progression of such disorders because they can be grown with a patient's specific genetic condition.
Mature stem cells can be extracted from a patient's blood sample and then reprogrammed to become like embryonic cells, able to turn into any type of cell in the body. It's akin to walking back the clock so that the cells regain unlimited potential for development. (The Japanese scientist who pioneered this technique was awarded the Nobel Prize in 2012.) These "induced pluripotent stem cells" can then be chemically coaxed to grow into mini kidneys that have the patient's genetic disorder.
"The (genetic) defects are quite clear in the organoids, and they can be monitored in the dish," Little says. To date, her research team has created organoids from 20 different stem cell lines.
Medication regimens can also be tested on the organoids, allowing specific tailoring for each patient. For now, such testing remains restricted to mice, but Little says it eventually will be done on human organoids so that the results can more accurately reflect how a given patient will respond to particular drugs.
Next Steps
Although these organoids cannot yet replace kidneys, Little says they may plug a huge gap in renal care by assisting in developing new treatments for chronic conditions. Currently, most patients with a serious kidney disorder see their options narrow to dialysis or organ transplantation. The former not only requires multiple sessions a week, but takes a huge toll on patient health.
Ten percent of older patients on dialysis die every year in the U.S. Aside from the physical trauma of organ transplantation, finding a suitable donor outside of a family member can be difficult.
"This is just another great example of the potential of pluripotent stem cells."
Meanwhile, the ongoing creation of organoids is supplying Little and her colleagues with enough information to create larger and more functional organs in the future. According to Little, researchers in the Netherlands, for example, have found that implanting organoids in mice leads to the creation of vascular growth, a potential pathway toward creating bigger and better kidneys.
And while Little acknowledges that creating a fully-formed custom organ is the ultimate goal, the mini organs are an important bridge step.
"This is just another great example of the potential of pluripotent stem cells, and I am just passionate to see it do some good."
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.