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

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 enginee...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the National Academy of Sciences - PNAS Vol. 115; no. 51; pp. 12920 - 12925
Main Authors Sokolov, I., Dokukin, M. E., Kalaparthi, V., Miljkovic, M., Wang, A., Seigne, J. D., Grivas, P., Demidenko, E.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 18.12.2018
Subjects
Artificial intelligence
Atomic force microscopy
Biological Sciences
Biopsy
Bladder
Bladder cancer
Body fluids
Cancer
Cells
Diagnostic systems
Diagnostic tests
Health care
Image detection
Learning algorithms
Machine learning
Medical imaging
Microscopy
Physical Sciences
Statistical analysis
Urine
noninvasive methods
atomic force microscopy
machine learning
diagnostic imaging
cancer diagnostics
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
5Present address: Harvard University, Cambridge, MA 02138.
3Present address: Department of Physics, National Research Nuclear University MEPhI, Moscow 115409, Russia.
Edited by David A. Weitz, Harvard University, Cambridge, MA, and approved November 5, 2018 (received for review September 24, 2018)
2M.E.D., V.K., and M.M. contributed equally to this work.
Author contributions: I.S., M.E.D., J.D.S., P.G., and E.D. designed research; I.S., M.E.D., V.K., M.M., and P.G. performed research; I.S. and M.E.D. contributed new reagents/analytic tools; I.S., V.K., M.M., and A.W. analyzed data; and I.S. wrote the paper.
4Deceased August 2, 2018.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1816459115