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TUESDAY, Dec. 4, 2018 (HealthDay News) — Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces can detect bladder cancer with high diagnostic accuracy, according to research published online Dec. 3 in the Proceedings of the National Academy of Sciences<\/i>.<\/p>\n
Igor Sokolov, Ph.D., from Tufts University in Medford, Massachusetts, and colleagues report on a diagnostic imaging approach based on nanoscale-resolution scanning of surfaces of cells collected from body fluids using a modality of atomic force microscopy (AFM), subresonance tapping, and machine-learning analysis. The surface parameters were used to classify cells. The method was applied to bladder cancer detection.<\/p>\n
The researchers found that when examining five cells per patient urine sample, the method showed 94 percent diagnostic accuracy, which was a statistically significant improvement compared with the currently used clinical standard, cystoscopy (P < 0.05). The results were verified in 43 control and 25 bladder cancer patients.<\/p>\n
“Our approach is based on the premises of field carcinogenesis, which has been established as a common event in many malignancies; in particular, in bladder cancer, in which both genetic and exogenous milieu risk factors lead not only to a localized tumor but may affect the entire organ area,” the authors write. “Our results provide evidence that the described method is sufficiently sensitive to detect this cancer signature.”<\/p>\n
Tufts University has applied for a patent protection for the AFM machine-learning method described in the paper, which was invented by two of the authors.<\/p>\n
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