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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationQuantitative Phase Imaging II
EditorsGabriel Popescu, YongKeun Park
PublisherSPIE
ISBN (Electronic)9781628419528
DOIs
StatePublished - Jan 1 2016
Event2nd Conference on Quantitative Phase Imaging, QPI 2016 - San Francisco, United States
Duration: Feb 14 2016Feb 17 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9718
ISSN (Print)1605-7422

Other

Other2nd Conference on Quantitative Phase Imaging, QPI 2016
CountryUnited States
CitySan Francisco
Period2/14/162/17/16

Keywords

  • Quantitative Phase Imaging
  • SLIM
  • automatic diagnosis
  • diagnosis
  • prostate cancer
  • spatial light interference microscopy

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Nguyen, T. H., Sridharan, S., Marcias, V., Balla, A. K., Do, M. N., & Popescu, G. (2016). Automatic Gleason grading of prostate cancer using SLIM and machine learning. In G. Popescu, & Y. Park (Eds.), Quantitative Phase Imaging II [97180Y] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9718). SPIE. https://doi.org/10.1117/12.2217288