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Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cancer
Author(s) -
Igor Sokolov,
Maxim Dokukin,
V. Kalaparthi,
Miloš D. Miljković,
Andrew Wang,
John D. Seigne,
Petros Grivas,
Eugene Demidenko
Publication year - 2018
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.1816459115
Subject(s) - medicine , bladder cancer , cancer , cancer detection , urethra , biopsy , pathology , diagnostic test , colorectal cancer , cervical cancer , urinary system , radiology , urology , emergency medicine
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.

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