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Date of Award
Spring 5-12-2021
Degree Name
Bachelor of Science
Restricted/Unrestricted
Restricted
Abstract
Prostate cancer (PCa) is among the top three most common cancers in men and has the second highest mortality rate. However, over-treatment of low grade PCas can be more harmful than just keeping it on surveillance. Hence, determination of PCa aggressiveness is crucial, but is difficult without biopsies due to convoluted signals from non-invasive methods. While DBSI metrics combined with deep neural networks (DNN) have been effective in differentiating PCa tissues, they lack in accuracy for Grade Group classification without using majority voting schema. In this study, a density-based medoid clustering model is used to detect hidden propensities in the isotropic diffusion spectrum between various PCa grades. Representative curves for each cluster were then used to predict PCa grades of other lesions to improve the diagnostic potential of prediction models. A modular neural network (MNN) was developed to combine cluster grading with the deep neural network to further increase diagnostic capabilities. Cluster grading proved to be effective, with accuracies of 76.8%, 71.2%, 81.2%, 83.3% and 87.3% for Grade Groups 1, 2, 3, 4 and 5 respectively. The developed MNN highly favors the Cluster grading model, suggesting that further improvement of DNNs is critical to increasing MNN potential.
Mentor
Dennis Barbour
Additional Advisors
Ron Cytron, Jonathan Silva
Recommended Citation
Wu, Anthony, "Modular Neural Network and Clustering using Diffusion Basis Spectrum Imaging (DBSI) metrics improves Prostate Cancer Grading" (2021). Senior Honors Papers / Undergraduate Theses. 36.
https://openscholarship.wustl.edu/undergrad_etd/36