Imagine a world where cancer surgery is significantly more precise, ensuring complete removal of cancerous tissue without the need for multiple, stressful operations. This is the promise of a groundbreaking new technique that's changing the way surgeons approach cancer treatment. But how is this possible? Let's dive in.
Traditionally, the primary goal of cancer surgery is to remove the cancerous tissue from the body. However, achieving this with complete accuracy has always been a significant challenge. Surgeons often rely on preoperative imaging techniques like ultrasounds and MRIs to locate the cancer. Then, they must wait for postoperative analysis of the removed tissue to confirm whether all cancerous cells have been eliminated.
The delicate balance for surgeons lies in removing all diseased tissue while preserving as much healthy tissue as possible. Consider lumpectomies for breast cancer, a conservative approach that, studies show, can be as effective as removing the entire breast. But here's where it gets controversial: current methods rely on postoperative pathology. If the analysis reveals cancer cells at the edges of the removed tissue, patients often face a second or even a third surgery. According to Lihong Wang, a professor at Caltech, up to one-third or more of breast cancer patients experience these repeat surgeries.
To tackle this problem, Wang, in collaboration with cancer specialists at the City of Hope, has pioneered a revolutionary method. This new approach allows for real-time analysis of excised tissues during surgery, guiding surgeons to remove tissue until all cancer is gone. But how does it work?
Traditional methods of analyzing tumor samples are quite complex. First, the tissue must be stabilized before it can be sliced and viewed under a microscope. This often involves freezing the sample, which can damage the tissue, especially fatty tissues like breast tissue. Another option is fixing the tissue in formalin and embedding it in paraffin, but this process takes time and can cause distortions.
After preparation, the tissue is sliced into thin sections and stained with two chemicals, hematoxylin and eosin (H&E). These stains highlight cell nuclei and the surrounding cytoplasm, helping pathologists differentiate between cancerous and healthy cells. Cancerous cells appear more densely packed, with larger nuclei.
Even with careful preparation, the accuracy of the analysis depends on the pathologist's skill, making it difficult to standardize results. And this is the part most people miss: this entire process is time-consuming, often resulting in patients waiting hours or even days for results.
Wang's new technique, called ultraviolet photoacoustic microscopy (UV-PAM), eliminates the need for all these time-consuming steps. This innovative approach skips freezing, fixing, slicing, staining, and even direct examination by pathologists.
UV-PAM uses a low-energy laser to excite the tissue. The laser's frequency is set to the absorption peak of nucleic acids (DNA and RNA), creating what Wang calls "Mother Nature's natural staining process." Cell nuclei absorb the laser's light and appear brighter. This absorption also generates ultrasonic sound waves, which allow for incredibly precise imaging, with a resolution of 200 to 300 nanometers. The image is then processed with artificial intelligence (AI) to mimic traditional H&E staining, making it easy for pathologists and surgeons to interpret without additional training.
The AI system has been trained on a vast database of tissue images, enabling it to provide an initial diagnosis. "This is where AI can shine," Wang explains, "because AI can examine the images as quickly as we acquire them. We can simultaneously analyze one area of the tumor while scanning the next one, which speeds up the process even more."
To be practical in the operating room, surgeons requested an analysis time limit of 10 minutes. Wang is confident that the imaging can be completed within 5 to 10 minutes, fast enough to guide surgical decisions before closing the incision. The technique also appears to be "tissue agnostic," meaning it works equally well on various tissue types like breast, bone, skin, and organs.
"We're still in the testing stage with this technology, but we hope to move toward a commercial product that can be widely used," Wang says.
This technology is detailed in an article titled "Rapid cancer diagnosis using deep-learning–powered label-free subcellular-resolution photoacoustic histology" published in Science Advances on November 21, 2025. The research was funded by the National Institutes of Health and the National Research Foundation of the Korean government.
What are your thoughts on this innovative approach? Do you think this technology will revolutionize cancer surgery? Share your opinions in the comments below!
