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Noninvasive Nanoscale Test Accurately Detects Bladder Cancer

Atomic force microscopy surface maps of bladder cells.

Researchers have developed an accurate, noninvasive diagnostic test that can be used for a number of cancers. Their success in applying this test to the detection of bladder cancer makes it the first to effectively utilize atomic force microscopy (AFM) for diagnostic purposes.

Bladder cancer is one of the most common cancers and one of the most common causes of cancer-related death. If it is detected in stage 0, bladder cancer has a five-year survival rate of 98%; if it is detected at an advanced stage, however, the five-year survival rate can drop as low as 15%.

"By introducing a noninvasive diagnostic method that is more accurate than the invasive visual examination, we could significantly decrease the cost and inconvenience to patients," stated Igor Sokolov, PhD, Professor of Mechanical Engineering and Biomedical Engineering at Tufts University School of Engineering and lead author of the study, which was published in Proceedings of the National Academy of Sciences (PNAS). "All that is needed is a urine sample, and not only could we more effectively monitor patients after treatment, we could also more easily screen healthy individuals who may have a family history of the disease and potentially detect the grade of cancer development. Determining the efficiency of early screening and grade detection is a separate, important task of our future research."

Atomic force microscopy is an extremely high-resolution form of scanning probe microscopy that has resolutions involving fractions of a nanometer. As AFM scans a surface with a minute cantilever that is deflected by bumps on the surface, it records those deflections, creating a topographical nanoscale map. The AFM cantilever's deflection patterns can indicate some physical properties of the surface. The researchers found that in bladder cells extracted from a urine sample, unique surface features can be used to differentiate cancerous cells from healthy ones, enabling the diagnosis of bladder cancer.

In 25 patients with bladder cancer and 43 healthy controls, the researchers' diagnostic method, which incorporates machine learning to achieve increased accuracy in recognizing surface features such as adhesion, roughness, directionality, and fractal properties, demonstrated 94% accuracy when examining five cells from each patient's urine sample. This level of accuracy is a statistically significant improvement over cystoscopy, the standard clinical method of diagnosis.

"AFM has been around for more than 30 years, but this is the first time it has shown promise for clinical diagnostics," remarked Dr. Sokolov. "The accuracy appears to be better than the current clinical standard for bladder cancer diagnosis, but we will need to test the method on a larger cohort of patients before it can be introduced into clinical practice."

The investigators believe that the test could also be used for the detection of other cancers in which cells or bodily fluids are available for analysis without invasive biopsy. "We are hopeful that AFM could ultimately be applied to the detection of other tumor types, such as gastrointestinal, colorectal, and cervical cancers," Dr. Sokolov stated.

In addition, the researchers say that their approach could be applied to identify other cell abnormalities unrelated to cancer. It could also monitor cellular reactions to drugs.

For More Information

Sokolov I, Dokukin ME, Kalaparthi V, et al (2018). Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: detection of bladder cancer. Proc Natl Acad Sci U S A. [Epub ahead of print] DOI:10.1073/pnas.1816459115

​Image credit: Igor Sokolov, Tufts University

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