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PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE: MULTI-INSTITUTIONAL USE CASES

Algohary, Ahmad
http://rave.ohiolink.edu/etdc/view?acc_num=case1599231033923829

Abstract Details

2020, Doctor of Philosophy, Case Western Reserve University, Biomedical Engineering.
At least 34% of prostate cancer (PCa) patients are diagnosed with low-risk disease, which makes them prospective candidates for active surveillance (AS) where these patients are routinely monitored via repeated Prostate-Specific Antigen measurements and biopsies for signs of disease progression. Prostate multiparametric-MRI (mpMRI) provides structural and functional measurements on multiple sequences for lesion characterization. It is widely used for diagnosing PCa which leads to the development of standardized guidelines for acquiring, reading and reporting findings through the Prostate Imaging-Reporting and Data System (PI-RADS). MpMRI has been proposed as a means of non-invasive monitoring of disease progression for patients on AS, yet, its inability to distinguish disease from tumor confounders and the inter-observer variability in terms of interpretation limits its complete adoption. Accurately classifying PCa tumors in addition to providing quantitative assessment of differences between AS patient groups who express different levels of PCa progression would not just obviate the need for biopsies and all the side effects that come with them, but also would pave the road for finding reliable solutions for the overdiagnosis and overtreatment of prostate cancer in the US. Radiomic analysis is the use of computer-extracted features for quantitatively characterizing subtle, sub-visual patterns associated with PCa. Features capturing intra- and peri-tumoral heterogeneity have been shown to distinguish low- from high-risk PCa. The sub-visual patterns captured by these features are not usually discernible to radiologists and hence potentially could add complementary information to the visual interpretation of the MRI. Common examples of radiomic features are first order statistics (mean, median, standard deviation, skewness, and kurtosis), grey-level co-occurrence (Haralick, Collage) and Laws’ energy. In this work, radiomic analysis of mpMRI allowed for 1) discriminating low- from high-risk PCa by identifying lesions misclassified by radiologists on routine mpMRI and 2) Predicting PCa upgrading and upstaging for patients on AS. The employed radiomics-based approach will have a substantial clinical impact in the management of PCa patients in: 1) better identifying which patients are candidates for AS, 2) which patients on AS are at risk for disease progression and hence might need definitive treatment.
Anant Madabhushi, PhD (Committee Chair)
David Wilson, PhD (Committee Member)
Lee Ponsky, MD (Committee Member)
Michael Kattan, PhD (Committee Member)
102 p.
Artificial Intelligence; Biomedical Engineering; Medical Imaging
Prostate Cancer Radiomics MRI Machine Learnings QDA

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Citations

  • Algohary, A. (2020). PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE: MULTI-INSTITUTIONAL USE CASES [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1599231033923829

    APA Style (7th edition)

  • Algohary, Ahmad. PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE: MULTI-INSTITUTIONAL USE CASES. 2020. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1599231033923829.

    MLA Style (8th edition)

  • Algohary, Ahmad. "PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE: MULTI-INSTITUTIONAL USE CASES." Doctoral dissertation, Case Western Reserve University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1599231033923829

    Chicago Manual of Style (17th edition)

case1599231033923829
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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.