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9 March 2016 Automatic Gleason grading of prostate cancer using SLIM and machine learning
Tan H. Nguyen, Shamira Sridharan, Virgilia Marcias, Andre K. Balla, Minh N. Do, Gabriel Popescu
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Proceedings Volume 9718, Quantitative Phase Imaging II; 97180Y (2016) https://doi.org/10.1117/12.2217288
Event: SPIE BiOS, 2016, San Francisco, California, United States
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.
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Tan H. Nguyen, Shamira Sridharan, Virgilia Marcias, Andre K. Balla, Minh N. Do, and Gabriel Popescu "Automatic Gleason grading of prostate cancer using SLIM and machine learning", Proc. SPIE 9718, Quantitative Phase Imaging II, 97180Y (9 March 2016); https://doi.org/10.1117/12.2217288
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Cited by 4 scholarly publications.
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KEYWORDS
Image segmentation
Tissues
Prostate cancer
Diagnostics
Cancer
Phase imaging
Prostate
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