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Machine-Based Interpretation and Classification of Image-Derived Features: Applications in Digital Pathology and Multi-Parametric MRI of Prostate Cancer

Ginsburg, Shoshana
http://rave.ohiolink.edu/etdc/view?acc_num=case1459116552

Abstract Details

2016, Doctor of Philosophy, Case Western Reserve University, Biomedical Engineering.
The analysis of medical images--from magnetic resonance imaging (MRI) to digital pathology--for disease characterization typically involves extraction of hundreds of features, which may be used to predict disease presence, aggressiveness, or outcome. Unfortunately, the dimensionality of the feature space poses a formidable challenge to the construction of robust classifiers for predicting disease presence and aggressiveness. In this work we present novel strategies to facilitate the construction of robust, interpretable classifiers when the dimensionality of the feature space is high. In the context of prostate cancer, we demonstrate the benefit of our approach for identifying (a) radiomic features that are useful for detecting prostate cancer on multi-parametric MRI, (b) radiomic features that predict the risk of prostate cancer recurrence on T2-weighted MRI, and (c) histomorphometric features describing cellular and glandular architecture on digital pathology images that predict the risk of prostate cancer recurrence post-treatment. In the context of breast cancer, we identify histomorphometric features describing cancer patterns in estrogen receptor positive (ER+) breast cancer tissue slides that can predict (a) which cancer patients will have recurrence following treatment with tamoxifen and (b) risk category as determined by a 21 gene expression assay called Oncotype DX. Additionally, we also investigate whether radiomic features characterizing prostate tumors that manifest in the peripheral zone of the prostate are different from radiomic features characterizing transition zone tumors, and we develop a novel approach for pharmacokinetic modeling on dynamic contrast-enhanced MRI that relies exclusively on prostate voxels, with no reliance on an arterial input function or reference tissue.
Anant Madabhushi (Advisor)
189 p.
Biomedical Engineering; Medical Imaging; Radiology
Prostate cancer; MRI; computer-aided diagnosis; dimensionality reduction

Recommended Citations

Citations

  • Ginsburg, S. (2016). Machine-Based Interpretation and Classification of Image-Derived Features: Applications in Digital Pathology and Multi-Parametric MRI of Prostate Cancer [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1459116552

    APA Style (7th edition)

  • Ginsburg, Shoshana. Machine-Based Interpretation and Classification of Image-Derived Features: Applications in Digital Pathology and Multi-Parametric MRI of Prostate Cancer. 2016. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1459116552.

    MLA Style (8th edition)

  • Ginsburg, Shoshana. "Machine-Based Interpretation and Classification of Image-Derived Features: Applications in Digital Pathology and Multi-Parametric MRI of Prostate Cancer." Doctoral dissertation, Case Western Reserve University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459116552

    Chicago Manual of Style (17th edition)

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© 2016, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.