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Proc Natl Acad Sci U S A. 2018 Dec 3. pii: 201816459. doi: 10.1073/pnas.1816459115. [Epub ahead of print]

Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cancer.

Author information

Department of Mechanical Engineering, Tufts University, Medford, MA 02155;
Department of Biomedical Engineering, Tufts University, Medford, MA 02155.
Department of Physics, Tufts University, Medford, MA 02155.
Department of Mechanical Engineering, Tufts University, Medford, MA 02155.
Division of Urology, Dartmouth-Hitchcock Medical Center, Hanover, NH 03756.
Department of Medicine, Division of Oncology, University of Washington, Seattle, WA 98105.
Biomedical Data Science, Geisel School of Medicine, Hanover, NH 03756.


We report an approach in diagnostic imaging based on nanoscale-resolution scanning of surfaces of cells collected from body fluids using a recent modality of atomic force microscopy (AFM), subresonance tapping, and machine-leaning analysis. The surface parameters, which are typically used in engineering to describe surfaces, are used to classify cells. The method is applied to the detection of bladder cancer, which is one of the most common human malignancies and the most expensive cancer to treat. The frequent visual examinations of bladder (cytoscopy) required for follow-up are not only uncomfortable for the patient but a serious cost for the health care system. Our method addresses an unmet need in noninvasive and accurate detection of bladder cancer, which may eliminate unnecessary and expensive cystoscopies. The method, which evaluates cells collected from urine, shows 94% diagnostic accuracy when examining five cells per patient's urine sample. It is a statistically significant improvement (P < 0.05) in diagnostic accuracy compared with the currently used clinical standard, cystoscopy, as verified on 43 control and 25 bladder cancer patients.


atomic force microscopy; cancer diagnostics; diagnostic imaging; machine learning; noninvasive methods


Conflict of interest statement

Conflict of interest statement: After acceptance of the manuscript in its current form, Tufts University has applied for a patent protection for the AFM machine-learning method described in this paper (invented by I. Sokolov and M. Miljkovic). Other authors declare no conflict of interest.

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