A 94% accurate diagnosis of bladder cancer using atomic force microscopy on urine.
Bladder cancer is among the most common and deadly of cancers. Because of its high recurrence rate (50-80 percent), patients must be monitored frequently for recurrence or progression of the disease. This monitoring currently consists of visual analysis of cells taken from the patient's bladder. It is uncomfortable, it is expensive, and it is not even especially accurate, detecting only around 60 percent of low-grade tumors.
Now, scientists have figured out how to use atomic force microscopy (AFM) to detect bladder cancer in urine samples. By analyzing only five cells, it can achieve 94 percent accuracy.
Use the force
Atomic force microscopy differs from optical microscopy in that it doesn't produce an image of the sample. Instead, a probe scans the sample and produces a topographical map of its surface with nanoscale resolution. In engineering, atomic force microscopy is usually used to describe surfaces like ceramic and glass, as it can analyze different properties of the surface, like its roughness, fractal nature, or magnetic behavior.
While AFM has been around for 30 years, people haven't figured out how to use it clinically.
Looking at a urine sample under a light microscope is convenient, but it's not an accurate diagnostic method. Not all urine samples from people with cancer will harbor cancer cells, and different observers may disagree on whether the cells they see are cancerous or not.
But atomic force microscopy doesn't rely on visual recognition of cancer cells; it examines parameters of the cellular surfaces, and it can measure the degree to which cells adhere to the scanning probe. This particular analysis also doesn't rely on observers. Instead, it relies on machine learning, in which an algorithm is trained to recognize the cancer.
Finding cancer without looking at it
What about the problem of not having a cancerous cell in the sample? Field cancerization is a phenomenon in which cancer alters the properties of the tissue around it. It's a particularly common effect in bladder cancer, so many cells in a patient's urine may bear the hallmarks of cancer on their surface even if they are not cancerous themselves. The new AFM approach uses this to distinguish between the surfaces of cells from normal and cancerous tissue even when cancerous cells aren't present. The cancer signature detected probably comes from changes in the glycocalyx, a protein and lipid sheath that surrounds the membranes of cells lining the bladder.
This noninvasive method should be applicable to the detection and diagnosis of other cancers if they have signatures that appear in bodily fluids; candidates include things like colorectal, gastrointestinal, cervical, and aerodigestive cancers. By altering the surface parameters, it might also be used to detect other cellular abnormalities and perhaps even to monitor sensitivity to and reaction to drugs. The most recent microscope harnessed by doctors may thus launch a whole new field of nano-diagnosis.
Fuente de la nota:
Fuente del artículo:
Sokolov, I. et al. (2018). Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cáncer. PNAS.