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

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Abstract
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. Significance New noninvasive and accurate diagnostic tests of cancer are important. Here we describe such a test, which is applied to the detection of bladder cancer, one of the most common cancers and cause of cancer-related deaths. This method can also be applied for the detection of other cancers, in which cells or body fluid are available for analysis without the need for invasive biopsy, e.g., upper urinary tract, urethra, colorectal and other gastrointestinal, cervical, aerodigestive cancers, etc. Furthermore, the described approach can be extended to detect cell abnormalities beyond cancer as well as to monitor cell reaction to various drugs (nanopharmacology). Thus, this approach may suggest a whole new direction of diagnostic imaging.
Funding Information
  • National Science Foundation (CMMI 1435655)
  • DHMC | Norris Cotton Cancer Center (Prouty funds)

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