Graduation Year

2015

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Lawrence Hall, Ph.D.

Co-Major Professor

Dmitry Goldgof, Ph.D.

Committee Member

Rangachar Kasturi, Ph.D.

Committee Member

Shuai Huang, Ph.D.

Committee Member

Hui Yang, Ph.D.

Committee Member

Robert Gatenby, M.D.

Keywords

Computer-aided diagnosis, Data Mining, Radiology

Abstract

Quantitative cancer imaging is an emerging field that develops computational techniques to acquire a deep understanding of cancer characteristics for cancer diagnosis and clinical decision making. The recent emergence of growing clinical imaging data provides a wealth of opportunity to systematically explore quantitative information to advance cancer diagnosis. Crucial questions arise as to how we can develop specific computational models that are capable of mining meaningful knowledge from a vast quantity of imaging data and how to transform such findings into improved personalized health care?

This dissertation presents a set of computational models in the context of malignant brain tumors— Giloblastoma Multiforme (GBM), which is notoriously aggressive with a poor survival rate. In particular, this dissertation developed quantitative feature extraction approaches for tumor diagnosis from magnetic resonance imaging (MRI), including a multi-scale local computational feature and a novel regional habitat quantification analysis of tumors. In addition, we proposed a histogram-based representation to investigate biological features to characterize ecological dynamics, which is of great clinical interest in evaluating tumor cellular distributions.

Furthermore, in regards to clinical systems, generic machine learning techniques are typically incapable of generalizing well to specific diagnostic problems. Therefore, quantitative analysis from a data-driven perspective is becoming critical. In this dissertation, we propose two specific data-driven models to tackle different types of clinical MRI data. First, we inspected cancer systems from a time-domain perspective. We propose a quantitative histogram-based approach that builds a prediction model, measuring the differences from pre- and post-treatment diagnostic MRI data. Second, we investigated the problem of mining knowledge from a skewed distribution—data samples of each survival group are unequally distributed. We proposed an algorithmic framework to effectively predict survival groups by jointly considering imbalanced distributions and classifier design. Our approach achieved an accuracy of 95.24%, suggesting it captures class-specific information in a challenging clinical setting.

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