@inproceedings{4ac386659c9b4e0099e358241e76b177,
title = "Automatic Gleason grading of prostate cancer using SLIM and machine learning",
abstract = "In this paper, we present an updated automatic diagnostic procedure for prostate cancer using quantitative phase imaging (QPI). In a recent report [1], we demonstrated the use of Random Forest for image segmentation on prostate cores imaged using QPI. Based on these label maps, we developed an algorithm to discriminate between regions with Gleason grade 3 and 4 prostate cancer in prostatectomy tissue. The Area-Under-Curve (AUC) of 0.79 for the Receiver Operating Curve (ROC) can be obtained for Gleason grade 4 detection in a binary classification between Grade 3 and Grade 4. Our dataset includes 280 benign cases and 141 malignant cases. We show that textural features in phase maps have strong diagnostic values since they can be used in combination with the label map to detect presence or absence of basal cells, which is a strong indicator for prostate carcinoma. A support vector machine (SVM) classifier trained on this new feature vector can classify cancer/non-cancer with an error rate of 0.23 and an AUC value of 0.83.",
keywords = "Quantitative Phase Imaging, SLIM, automatic diagnosis, diagnosis, prostate cancer, spatial light interference microscopy",
author = "Nguyen, {Tan H.} and Shamira Sridharan and Virgilia Marcias and Balla, {Andre K.} and Do, {Minh N.} and Gabriel Popescu",
note = "This work was supported by the National Science Foundation (CBET-1040461, IIP-1353368) and Agilent Laboratories. For more information, visit http://light.ece.Illinois.edu; 2nd Conference on Quantitative Phase Imaging, QPI 2016 ; Conference date: 14-02-2016 Through 17-02-2016",
year = "2016",
doi = "10.1117/12.2217288",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Gabriel Popescu and YongKeun Park",
booktitle = "Quantitative Phase Imaging II",
}