Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

Tan H Nguyen, Shamira Sridharan, Virgilia Macias, Andre Kajdacsy-Balla, Jonathan Melamed, Minh N. Do, Gabriel Popescu

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.