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Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer

Penzias, Gregory
http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117

Abstract Details

2017, Master of Sciences (Engineering), Case Western Reserve University, Biomedical Engineering.
Radiomics has shown promise for in vivo prediction of cancer risk, thus providing a potential avenue for reducing over-treatment and unnecessarily invasive biopsy-based diagnosis. Radiomics could be particularly beneficial for stratifying patients into different risk groups in the context of prostate cancer (PCa), for which limitations of current in vivo risk assessment result in over-diagnosis and over-treatment. Despite its promise, successful translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. Few studies, however, have attempted to establish the biological or histomorphometric basis for the performance of radiomics. Accomplishing this requires fusing the information obtained from the imaging modalities of radiology and histopathology, since the gold standard definition of PCa comes from histopathologic analysis of whole-mount specimens. The first step in performing this radiology-pathology fusion in PCa entails achieving spatial correspondence between preoperative in vivo magnetic resonance imaging (MRI) and ex vivo hematoxylin & eosin (H&E)-stained whole-mount radical prostatectomy specimens via deformable co-registration. Co-registration, however, requires whole-mount histology sections (WMHSs), which are not always feasible to obtain. In such cases, large specimens are cut into multiple smaller tissue fragments. This thesis presents work on two related modules of radiology-pathology fusion in PCa: First, a novel automated program called AutoStitcher, which reconstructs pseudo whole-mount histology sections (PWMHSs) by digitally stitching together multiple smaller tissue fragments, thus enabling co-registration with in vivo radiographic imagery. AutoStitcher reconstructed PWMHSs with less than 3% error relative to manually stitched PWMHSs. Second, comprehensive sets of radiomic features extracted from MRI and quantitative histomorphometric features from H&E were extracted and then spatially co-localized to characterize each tumor region. Correlative analysis revealed a set of promising predictive radiomic markers that could accurately distinguish low- from intermediate-/high-risk PCa and a set of QH features that may form their histomorphometric basis. Results were validated on an independent dataset from a different institution.
Anant Madabhushi (Advisor)
Satish Viswanath (Committee Member)
David Wilson (Committee Member)
84 p.
Biomedical Engineering; Computer Science; Engineering; Medical Imaging; Oncology; Radiology
radiomics; quantitative histomorphometry; prostate cancer; imaging biomarkers; digital pathology; data fusion; computer vision; image reconstruction; image stitching

Recommended Citations

Citations

  • Penzias, G. (2017). Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117

    APA Style (7th edition)

  • Penzias, Gregory. Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer. 2017. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117.

    MLA Style (8th edition)

  • Penzias, Gregory. "Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer." Master's thesis, Case Western Reserve University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117

    Chicago Manual of Style (17th edition)

case1473415195867117
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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.