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

Abstract

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.