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Shao, Yanan

University of British Columbia

This thesis focuses on the automatic analysis of radiological and pathological images for cancer detection and classification. It addresses ultrasound imaging for prostate and breast cancer, and histopathology analysis for prostate cancer, where we propose several classification approaches based on novel features and deep learning. The goal of this thesis is to develop machine learning methods to assist clinicians in diagnosis, prognosis, and treatment planning for patients. To tackle the inherent data heterogeneity in prostate cancer research, we develop a novel framework based on the generative adversarial network to discard extraneous information. For benign vs. malignant classification, it achieves area-under-the-curve of 93.4%, sensitivity of 95.1%, and specificity of 87.7%, respectively, representing significant improvements of 5.0%, 3.9%, and 6.0% compared to using heterogeneous data. We propose novel methods that improve prostate cancer classification and risk stratification using multi-stain digital histopathology. For classification, we demonstrate that: (1) other stain types (Ki67, P63) improve classification performance upon H&E; (2) even without the presence of Ki67 and P63, by mimicking the stain types, H&E stain can better report the presence and severity of prostate cancer. For risk stratification, our proposed risk stratification pipeline, integrating clinicopathologic data and learned image features from multi-stain digital histopathology, outperforms the currently most common grading system, the Gleason grading system, in predicting clinical outcomes such as metastasis-free and overall survival. Using our risk models, 3.9% of low-risk patients are reclassified as high-risk and 21.3% of high-risk patients are reclassified as low-risk. These results demonstrate our risk stratification pipeline’s potential to guide the administration of adjuvant therapy after radical prostatectomy. For breast cancer, we propose a novel automatic pipeline for data processing, feature extraction, feature selection, and classification using ultrasound data. Our best results (95% confidence interval, area-under-the-curve = 95%±1.45%, sensitivity = 95%, and specificity = 93%) outperform the state-of-the-art results using shear wave absolute vibro-elastography. Moreover, our study proposes novel directions in the field of elasticity imaging for tissue classification. All the proposed methods have been tested on held-out sets and have demonstrated promising results, which would be useful in future cancer diagnosis, prognosis, and patient management.

Text

2021-11-10

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/80186

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/