Brain Cancer Chromosomes. Chromosomes prepared from a malignant glioblastoma visualized by spectral karyotyping (SKY) reveal an enormous degree of chromosomal instability -- a hallmark of cancer. Created by Thomas Ried, 2014
Photo by National Cancer Institute on Unsplash
How It Works
“The whole idea originally came from a conversation I had with General Norman Schwarzkopf, a supposedly brilliant military strategist,” says David Baskin, professor of neurosurgery and leader of the effort at Houston Methodist. “I asked him what is the secret to your success and he said, ‘Energy. Take out the power grid and the enemy can't communicate.’ So I thought about what supplies [energy to] cancer, especially brain cancer.”
Baskin came up with the idea of targeting the mitochondria, which process and produce energy for cancer cells.
"This is the most exciting thing in glioblastoma treatment I've seen since I've been a neurosurgeon, but it is very preliminary,” Baskin says.
The magnetic helmet creates a powerful oscillating magnetic field. At a set range of frequencies and timings, it disrupts the flow of electrons in the mitochondria of cancer cells. This leads to a release of certain chemicals called Reactive Oxygen Species, or ROS. In normal cells, this excess ROS is much lower, and it's neutralized by other chemicals called antioxidants.
However, cancer cells already have more ROS: they grow rapidly and uncontrollably, so their mitochondria need to produce more energy which in turn generates more ROS. By using the powerful magnetic field, levels of ROS get so high that the malignant cells are torn apart.
The biggest challenge was working out the specific range of frequencies and timing parameters they needed to use to kill cancer cells. It took skill, intuition, luck and lots of experiments. The helmet could theoretically be used to treat all types of glioblastoma.
Developing the magnetic helmet was a collaborative process. Santosh Helekar is a neuroscientist at Houston Methodist Research Institute and the director of oncomagnetics (magnetic cancer therapies) at the Peak Center in Houston Methodist Hospital. His previous invention with colleagues gave the team a starting point to build on. “About 7 years back I developed a portable brain magnetic stimulation device to conduct brain research,” Helekar says. “We [then] conducted a pilot clinical trial in stroke patients. The results were promising.”
Helekar presented his findings to neurosurgeons including Baskin. They decided to collaborate. With a team of scientists behind them, they modified the device to kill cancer cells.
The magnetic helmet studied for treatment of glioblastoma
Dr. David Baskin
Initial Results
After success in the lab, the team got FDA approval to conduct a compassionate trial in a 53-year-old man with end-stage glioblastoma. He had tried every other treatment available. But within 30 days of using the magnetic helmet his tumor shrank by 31%.
Sadly, 36 days into the treatment, the patient had an unrelated head injury due to a fall. The treatment was paused and he later died of the injury. Autopsy results of his brain highlighted the dramatic reduction in tumor cells.
Baskin says, “This is the most exciting thing in glioblastoma treatment I've seen since I've been a neurosurgeon, but it is very preliminary.”
The helmet is part of a growing number of non-invasive cancer treatments. One device that is currently being used by glioblastoma patients is Optune. It uses electric fields called tumor treating fields to slow down cell division and has been through a successful phase 3 clinical trial.
The magnetic helmet has the promise to be another useful non-invasive treatment according to Professor Gabriel Zada, a neurosurgeon and director of the USC Brain Tumor Center. “We're learning that various electromagnetic fields and tumor treating fields appear to play a role in glioblastoma. So there is some precedent for this though the tumor treating fields work a little differently. I think there is major potential for it to be effective but of course it will require some trials.”
Professor Jonathan Sherman, a neurosurgeon and director of neuro-oncology at West Virginia University, reiterates the need for further testing. “It sounds interesting but it’s too early to tell what kind of long-term efficacy you get. We do not have enough data. Also if you’re disrupting [the magnetic field] you could negatively impact a patient. You could be affecting the normal conduction of electromagnetic activity in the brain.”
The team is currently extending their research. They are now testing the treatment in two other patients with end-stage glioblastoma. The immediate challenge is getting FDA approval for those at an earlier stage of the disease who are more likely to benefit.
The Future
Baskin and the team are designing a clinical trial in the U.S., .U.K. and Germany. After positive results in cell cultures, they’re in negotiations to collaborate with other researchers in using the technology for lung and breast cancer. With breast cancer, the soft tissue is easier to access so a magnetic device could be worn over the breast.
“My hope is to develop a treatment to treat and hopefully cure glioblastoma without radiation or chemotherapy,” Baskin says. “We're onto a strategy that could make a huge difference for patients with this disease and probably for patients with many other forms of cancer.”
This article first appeared on Leaps.org on January 21, 2022.
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
The Lancet Oncology
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.