title: Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning.
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.
authors.affiliation: University of Illinois, Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering, Quantitative Light Imaging Laboratory, Urbana-Champaign, Illinois, United States.
authors.forename: Tan H
authors.initials: TH
authors.lastname: Nguyen
authors.affiliation: University of Illinois, Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering, Quantitative Light Imaging Laboratory, Urbana-Champaign, Illinois, United States.
authors.forename: Shamira
authors.initials: S
authors.lastname: Sridharan
authors.affiliation: University of Illinois, Department of Pathology, Chicago, Illinois, United States.
authors.forename: Virgilia
authors.initials: V
authors.lastname: Macias
authors.affiliation: University of Illinois, Department of Pathology, Chicago, Illinois, United States.
authors.forename: Andre
authors.initials: A
authors.lastname: Kajdacsy-Balla
authors.affiliation: New York University, School of Medicine, Department of Pathology, New York, New York, United States.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Melamed
authors.affiliation: University of Illinois, Department of Electrical and Computer Engineering, Computational Imaging Group, Coordinated Science Laboratory, Urbana-Champaign, Illinois, United States.
authors.forename: Minh N
authors.initials: MN
authors.lastname: Do
authors.affiliation: University of Illinois, Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering, Quantitative Light Imaging Laboratory, Urbana-Champaign, Illinois, United States.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Popescu
mesh_terms: D000465:Algorithms; D001706:Biopsy; D006801:Humans; D000069550:Machine Learning; D008297:Male; D060787:Neoplasm Grading / Q000379:methods*; D011471:Prostatic Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*