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Proceedings Paper

Prostate cancer detection from model-free T1-weighted time series and diffusion imaging
Author(s): Nandinee Fariah Haq; Piotr Kozlowski; Edward C. Jones; Silvia D. Chang; S. Larry Goldenberg; Mehdi Moradi
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Paper Abstract

The combination of Dynamic Contrast Enhanced (DCE) images with diffusion MRI has shown great potential in prostate cancer detection. The parameterization of DCE images to generate cancer markers is traditionally performed based on pharmacokinetic modeling. However, pharmacokinetic models make simplistic assumptions about the tissue perfusion process, require the knowledge of contrast agent concentration in a major artery, and the modeling process is sensitive to noise and fitting instabilities. We address this issue by extracting features directly from the DCE T1-weighted time course without modeling. In this work, we employed a set of data-driven features generated by mapping the DCE T1 time course to its principal component space, along with diffusion MRI features to detect prostate cancer. The optimal set of DCE features is extracted with sparse regularized regression through a Least Absolute Shrinkage and Selection Operator (LASSO) model. We show that when our proposed features are used within the multiparametric MRI protocol to replace the pharmacokinetic parameters, the area under ROC curve is 0.91 for peripheral zone classification and 0.87 for whole gland classification. We were able to correctly classify 32 out of 35 peripheral tumor areas identified in the data when the proposed features were used with support vector machine classification. The proposed feature set was used to generate cancer likelihood maps for the prostate gland.

Paper Details

Date Published: 20 March 2015
PDF: 9 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142X (20 March 2015); doi: 10.1117/12.2082337
Show Author Affiliations
Nandinee Fariah Haq, The Univ. of British Columbia (Canada)
Piotr Kozlowski, The Univ. of British Columbia (Canada)
Edward C. Jones, The Univ. of British Columbia (Canada)
Silvia D. Chang, The Univ. of British Columbia (Canada)
S. Larry Goldenberg, The Univ. of British Columbia (Canada)
Mehdi Moradi, IBM Almaden Research Ctr. (United States)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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