Graduation Semester and Year

2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Biomedical Engineering

Department

Bioengineering

First Advisor

Joseph A Maldjian

Second Advisor

Hanli Liu

Abstract

Gliomas account for the most common malignant primary brain tumors in both pediatric and adult populations. They arise from glial cells and are divided into low grade and high-grade gliomas with significant differences in patient survival. Patients with aggressive high-grade gliomas have life expectancies of less than 2 years. Glioblastoma (GBM) are aggressive brain tumors classified by the world health organization (WHO) as stage IV brain cancer. The overall survival for GBM patients is poor and is in the range of 12 to 15 months. These tumors are typically treated by surgery, followed by radiotherapy and chemotherapy. Gliomas often consist of active tumor tissue, necrotic tissue, and surrounding edema. Magnetic Resonance Imaging (MRI) is the most commonly used modality to assess brain tumors because of its superior soft tissue contrast. MRI tumor segmentation is used to identify the subcomponents as enhancing, necrotic or edematous tissue. Due to the heterogeneity and tissue relaxation differences in these subcomponents, multi-parametric (or multi-contrast) MRI is often used for accurate segmentation. Manual brain tumor segmentation is a challenging and tedious task for human experts due to the variability of tumor appearance, unclear borders of the tumor and the need to evaluate multiple MR images with different contrasts simultaneously. In addition, manual segmentation is often prone to significant intra- and inter-rater variability. To address these issues, Chapter 2 of my dissertation aims at designing and developing a highly accurate, 3D Dense-Unet Convolutional Neural Network (CNN) for segmenting brain tumors into subcomponents that can easily be incorporated into a clinical workflow. Primary brain tumors demonstrate broad variations in imaging features, response to therapy, and prognosis. It has become evident that this heterogeneity is associated with specific molecular and genetic profiles. For example, isocitrate dehydrogenase 1 and 2 (IDH 1/2) mutated gliomas demonstrate increased survival compared to wild-type gliomas with the same histologic grade. Identification of the IDH mutation status as a marker for therapy and prognosis is considered one of the most important recent discoveries in brain glioma biology. Additionally, 1p/19q co-deletion and O6-methyl guanine-DNA methyltransferase (MGMT) promoter methylation is associated with differences in response to specific chemoradiation regimens. Currently, the only reliable way of determining a molecular marker is by obtaining glioma tissue either via an invasive brain biopsy or following open surgical resection. Although the molecular profiling of gliomas is now a routine part of the evaluation of specimens obtained at biopsy or tumor resection, it would be helpful to have this information prior to surgery. In some cases, the information would aid in planning the extent of tumor resection. In others, for tumors in locations where resection is not possible, and the risk of a biopsy is high, accurate delineation of the molecular and genetic profile of the tumor might be used to guide empiric treatment with radiation and/or chemotherapy. The ability to non-invasively profile these molecular markers using only T2w MRI has significant implications in determining therapy, predicting prognosis, and feasible clinical translation. Thus, Chapters 3, 4 and 5 of my dissertation focuses on developing and evaluating deep learning algorithms for non-invasive profiling of molecular markers in brain gliomas using T2w MRI only. This includes developing highly accurate fully automated deep learning networks for, (i) classification of IDH mutation status (Chapter 3), (ii) classification of 1p/19q co-deletion status (Chapter 4), and (iii) classification of MGMT promoter status in Brain Gliomas (Chapter 5). An important caveat of using MRI is the effects of degradation on the images, such as motion artifact, and in turn, on the performance of deep learning-based algorithms. Motion artifacts are an especially pervasive source of MR image quality degradation and can be due to gross patient movements, as well as cardiac and respiratory motion. In clinical practice, these artifacts can interfere with diagnostic interpretation, necessitating repeat imaging. The effect of motion artifacts on medical images and deep learning based molecular profiling algorithms has not been studied systematically. It is likely that motion corruption will also lead to reduced performance of deep-learning algorithms in classifying brain tumor images. Deep learning based brain tumor segmentation and molecular profiling algorithms generally perform well only on specific datasets. Clinical translation of such algorithms has the potential to reduce interobserver variability, and improve planning for radiation therapy, improve speed & response to therapy. Although these algorithms perform very well on several publicly available datasets, their generalization to clinical datasets or tasks have been poor, preventing easy clinical translation. Thus, Chapter 6 of my dissertation focuses on evaluating the performance of the molecular profiling algorithms on motion corrupted, motion corrected and clinical T2w MRI. This includes, (i) evaluating the effect of motion corruption on the molecular profiling algorithms, (ii) determining if deep learning-based motion correction can recover the performance of these algorithms to levels similar to non-corrupted images, and (iii) evaluating the performance of these algorithms on clinical T2w MRI before & after motion correction. This chapter is an investigation on the effects of induced motion artifact on deep learning-based molecular classification, and the relative importance of robust correction methods in recovering the accuracies for potential clinical applicability. Deep-learning studies typically require a very large amount of data to achieve good performance. The number of subjects available from the TCIA database is relatively small when compared to the sample sizes typically required for deep learning. Despite this caveat, the data are representative of real-world clinical experience, with multiparametric MR images from multiple institutions, and represents one of the largest publicly available brain tumor databases. Additionally, the acquisition parameters and imaging vendor platforms are diverse across the imaging centers contributing data to TCIA. This study provides a framework for training, evaluating, and benchmarking any new artifact-correction architectures for potential insertion into a workflow. Although our results show promise for expeditious clinical translation, it will be essential to train and validate the algorithms using additional independent datasets. Thus, Chapter 7 of my dissertation discusses the limitations and possible future directions for this work.

Disciplines

Biomedical Engineering and Bioengineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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