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Title: Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis
Authors: Radhakrishnan, A
Damodaran, K 
Soylemezoglu, A.C
Uhler, C
Shivashankar, G.V 
Keywords: tumor marker
artificial neural network
cell nucleus
computer assisted diagnosis
diagnostic imaging
fluorescence imaging
tumor cell line
Biomarkers, Tumor
Cell Line, Tumor
Cell Nucleus
Deep Learning
Image Interpretation, Computer-Assisted
Neural Networks (Computer)
Optical Imaging
Issue Date: 2017
Publisher: Nature Publishing Group
Citation: Radhakrishnan, A, Damodaran, K, Soylemezoglu, A.C, Uhler, C, Shivashankar, G.V (2017). Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis. Scientific Reports 7 (1) : 17946. ScholarBank@NUS Repository.
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. © 2017 The Author(s).
Source Title: Scientific Reports
ISSN: 2045-2322
DOI: 10.1038/s41598-017-17858-1
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