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30 March 2017 Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning
Tan Huu Nguyen, Shamira Sridharan, Virgilia Macias, Andre Kajdacsy-Balla, Jonathan Melamed, Minh N. Do, Gabriel Popescu
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Journal of Biomedical Optics, Vol. 22, Issue 3, 036015 (March 2017). https://doi.org/10.1117/1.JBO.22.3.036015
Abstract
We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2017/$25.00 © 2017 SPIE
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Tan Huu Nguyen, Shamira Sridharan, Virgilia Macias, Andre Kajdacsy-Balla, Jonathan Melamed, Minh N. Do, and Gabriel Popescu "Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning," Journal of Biomedical Optics 22(3), 036015 (30 March 2017). https://doi.org/10.1117/1.JBO.22.3.036015
Received: 12 January 2017; Accepted: 13 March 2017; Published: 30 March 2017
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KEYWORDS
Image segmentation
Prostate cancer
Phase imaging
Prostate
Distortion
Tissues
Biopsy
CHORUS Article. This article was made freely available starting 30 March 2018
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