Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Biomedical Engineering

Supervisor

James C. Lacefield

Abstract

Ultrasound-based microvascular imaging is a promising technique for evaluating tumor response to antiangiogenic therapy in preclinical settings. However, challenges such as tissue motion and noise can hinder the accuracy and reliability of contrast-free ultrasound imaging. Additionally, there is a lack of consensus on how to best combine different microvascular ultrasound techniques, like contrast-free and contrast-enhanced ultrasound, for detecting treatment response in cancer models. To address these challenges, this thesis proposes an optimal shrinkage singular value decomposition (SVD) based clutter filtering method. The proposed method significantly enhances visualization and microvascular quantification by increasing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

Additionally, a scalable preclinical tumor model is presented using ex-ovo chick chorioallantoic membrane (CAM) tumor model and machine learning algorithms. The model aims to classify renal cell carcinoma (RCC) tumor cell response to antiangiogenic treatment based on ultrasound microvascular and perfusion parameters. Perfusion parameters derived from optimal shrinkage SVD-based contrast-free ultrasound and statistical analysis of contrast-enhanced ultrasound, along with microvascular parameters from conventional analysis, are evaluated. Feature selection algorithm identifies the best combination of ultrasound-based perfusion parameters for classification. The study expands from using control and treatment groups of a sensitive cell line to using two different cell lines with varying sensitivity levels. The model pipeline is also tested on an independent cell line with unknown sensitivity to the machine learning model.

The results demonstrate the effectiveness of the model in studying antiangiogenic treatment response using ultrasound microvascular imaging. The newly developed analysis for contrast-free and contrast-enhanced ultrasound improves classification results, and the model performs well with a separate test set, demonstrating its generalization capabilities and robustness. Therefore, the proposed model pipeline has the potential to evaluate treatment response in other tumor cells and preclinical translation.

In summary, this thesis highlights that optimal shrinkage SVD-based clutter filtering method improves microvascular quantification and its vascular quantification parameters when used along with other contras-free and contrast-enhanced perfusion parameters improves the classification of the RCC tumor responses to antiangiogenic treatment in the proposed tumor chick CAM model. The results demonstrate the robustness of this study, and its potential for broader preclinical applications.

Summary for Lay Audience

Ultrasound imaging holds promise for investigating the response of tumors to a type of therapy that aims to degrade the microvessels. However, there are challenges that can compromise the accuracy of ultrasound images in detecting microvessels. Furthermore, there is currently no established method for determining the optimal combination of ultrasound-based microvsessel parameters to effectively assess treatment efficacy. This thesis proposes a novel approach to enhance the ultrasound images in detection and quantification of mircovessels, facilitating the observation and measurement of tumor blood vessels.

Additionally, this study introduces a scalable preclinical tumor model utilizing machine learning models and is based on ultrasound-based microvascular parameters. The objective of the model is to assess the response of kidney cancer tumor cells to anti-angiogenic treatment by analyzing different ultrasound images and conducting blood flow measurements within the tumor. Initially, blood flow measurements and vascular quantifications were computed for a responsive tumor cell to the treatment used in this study for both the treated group and non-treated group. This analysis was subsequently expanded to include two distinct tumor types, each exhibiting different responses to treatment. Moreover, the model was evaluated using a new tumor cells to ascertain its ability to accurately predict treatment response.

The study demonstrates that the proposed model is valuable for evaluating the efficacy of the treatment in inhibiting tumor blood supply using ultrasound imaging. Furthermore, the newly proposed method for enhancing ultrasound imaging exhibits improved discriminative capabilities for tumors with varying treatment responses, as facilitated by machine learning. The model also demonstrates robust performance when tested with independent tumor cells, suggesting its potential for assessing treatment responses in other tumor types. Overall, this model has the potential to aid in the study and translation of preclinical cancer treatments.

In summary, this thesis establishes that the proposed method enhances the measurement of tumor blood vessels in a specific ultrasound imaging modality. When combined with other ultrasound imaging techniques, it augments the model's ability to differentiate between tumor responses to treatment. The study yields robust results, indicating that this approach holds promise for broader preclinical applications in evaluating tumor responses to treatment.

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