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Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis
- Source :
- Scientific Reports, Vol 7, Iss 1, Pp 1-13 (2017), Scientific Reports
- Publication Year :
- 2017
- Publisher :
- Springer Science and Business Media LLC, 2017.
-
Abstract
- Current cancer diagnosis employs various nuclear morphometric measures. While these have allowed accurate late-stage prognosis, early diagnosis is still a major challenge. Recent evidence highlights the importance of alterations in mechanical properties of single cells and their nuclei as critical drivers for the onset of cancer. We here present a method to detect subtle changes in nuclear morphometrics at single-cell resolution by combining fluorescence imaging and deep learning. This assay includes a convolutional neural net pipeline and allows us to discriminate between normal and human breast cancer cell lines (fibrocystic and metastatic states) as well as normal and cancer cells in tissue slices with high accuracy. Further, we establish the sensitivity of our pipeline by detecting subtle alterations in normal cells when subjected to small mechano-chemical perturbations that mimic tumor microenvironments. In addition, our assay provides interpretable features that could aid pathological inspections. This pipeline opens new avenues for early disease diagnostics and drug discovery.
- Subjects :
- 0301 basic medicine
Fluorescence-lifetime imaging microscopy
lcsh:Medicine
Computational biology
Biology
Article
03 medical and health sciences
Deep Learning
Cell Line, Tumor
Neoplasms
Image Interpretation, Computer-Assisted
Biomarkers, Tumor
medicine
Humans
lcsh:Science
Cell Nucleus
Tumor microenvironment
Multidisciplinary
Drug discovery
Optical Imaging
lcsh:R
Early disease
Cancer
medicine.disease
030104 developmental biology
Cancer cell
lcsh:Q
Neural Networks, Computer
Cancer cell lines
Human breast
Subjects
Details
- ISSN :
- 20452322
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....c2cafa3087a0ee4fd343ebe70db559b6
- Full Text :
- https://doi.org/10.1038/s41598-017-17858-1