Changhuei Yang, Ph.D

For decades, scientists and pathologists have tried, without much success, to come up with a way to determine which individual lung cancer patients are at greatest risk of having their illness spread, or metastasize, to other parts of the body. Now a team of scientists from Caltech and Washington University School of Medicine in St. Louis has fed that problem to artificial intelligence (AI) algorithms, asking computers to predict which cancer cases are likely to metastasize. In a novel pilot study of non-small cell lung cancer (NSCLC) patients, AI outperformed expert pathologists in making such predictions.

These predictions about the progression of lung cancer have important implications in terms of an individual patient’s life. Physicians treating early-stage NSCLC patients face the extremely difficult decision of whether to intervene with expensive, toxic treatments, such as chemotherapy or radiation, after a patient undergoes lung surgery. In some ways, this is the more cautious path because more than half of stage I–III NSCLC patients eventually experience metastasis to the brain. But that means many others do not. For those patients, such difficult treatments are wholly unnecessary… Continue reading.

Imagine driving home after a long day at work. Suddenly, a car careens out of an obscured side street and turns right in front of you. Luckily, your autonomous car saw this vehicle long before it came within your line of sight and slowed to avoid a crash. This might seem like magic, but a novel technique developed at Caltech could bring it closer to a reality.

With the advent of autonomous vehicles, advanced spacecraft, and other technologies that rely on sensors for navigation, there is an ever-increasing need for advanced technologies that can scan for obstacles, pedestrians, or other objects. But what if something is hidden behind another object… Continue reading.

Caltech researchers have developed a technique that combines fluorescence and ultrasound to peer through opaque media, such as biological tissue.

“We hope that one day this method can be deployed to extend the operating depth of fluorescence microscopy and help image fluorescent labeled cells deep inside living animals,” says Changhuei Yang, Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering, and senior author of a paper about the technique, dubbed fluorescence and ultrasound-modulated light correlation (FLUX for short, with the “X” for “correlation”), that was published on May 11 in Nature Photonics.

Scientists have long used glowing fluorescent probes for biological imaging; for example, tagging specific neurons that are then imaged for neuroscience studies. While fluorescence microscopy can provide nano- to microscale resolution, the resolution decreases rapidly along depth into biological tissue, because most biological tissue is opaque. As such, imaging fluorescent targets with high resolution, deep in tissue, has been a challenging task similar to seeing through fog… Continue reading.