Exploring Radiomics and Unveiling Novel Qualitative Imaging Biomarkers for Glioma Diagnosis in Dogs
| dc.contributor.author | Garcia Mora, Josefa Karina | en |
| dc.contributor.committeechair | Rossmeisl, John H. | en |
| dc.contributor.committeemember | Parker, Rell Lin | en |
| dc.contributor.committeemember | Daniel, Gregory B. | en |
| dc.contributor.committeemember | Zimmerman, Kurt L. | en |
| dc.contributor.department | Biomedical and Veterinary Sciences | en |
| dc.date.accessioned | 2025-01-08T09:00:36Z | en |
| dc.date.available | 2025-01-08T09:00:36Z | en |
| dc.date.issued | 2025-01-07 | en |
| dc.description.abstract | Radiomics integrates machine learning (ML) and radiology to extract and analyze quantitative features from medical imaging modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ultrasound (US) and digital radiographs (DX). By extracting pixel/voxel-level data, followed by standardization and feature selection, radiomics enables ML algorithms to assist in diagnosis and prognosis. While extensively researched in human medicine its application in veterinary medicine remains limited. Radiomics offers objective, data-driven insights, surpassing qualitative evaluations by revealing micromolecular disease features invisible to the human eye. Radiomics holds significant promise for diagnosing gliomas (GM), a challenging brain tumor where histopathology, the diagnostic gold standard, is seldom performed in veterinary medicine due to logistical and financial barriers, and it is also limited by inherent pathologist subjectivity and disagreement. Additionally, qualitative MRI demonstrates limited accuracy in identifying GM type and grade. By offering non-invasive and reproducible diagnostic and prognostic solutions, radiomics has the potential to overcome these challenges, enhancing brain tumor evaluation in both veterinary and human medicine. The primary goal of this study is to enhance the diagnosis and prognosis of GM by exploring both conventional and innovative non-invasive imaging techniques, with a focus on qualitative and quantitative MRI approaches. We hypothesize that quantitative and novel qualitative methods will surpass conventional expert qualitative assessments in accurately diagnosing GM type, grade, and progression. By doing so, we aim to improve the precision of GM imaging diagnoses, offering clinicians a more accessible and reliable tool to support their diagnostic and treatment decisions. Chapter 1 of this dissertation presents a comprehensive review of the challenges associated with diagnosing GM using MRI. It also introduces principles of radiomics, a novel and relatively underexplored field in veterinary medicine centered on quantitative imaging analysis for diagnostic and prognostic purposes. This includes an in-depth discussion of the radiomics workflow and associated ML methods. Chapter 2 demonstrates the use and efficacy of quantitative MRI for determination of GM size and therapeutic response assessments using both linear and volumetric techniques. Chapter 3 investigates the T2-weighted–FLAIR mismatch sign (T2FMM) in dogs, a well-established imaging biomarker of human low-grade astrocytomas, and demonstrates that the T2FMM is a highly specific biomarker for oligodendrogliomas —the first such imaging biomarker for GM to be discovered in veterinary research. Finally, Chapter 4 illustrates a structured radiomics pipeline for the standardized quantitative analysis of brain tumors on MRI and demonstrates that the use of radiomics ML models results in superior ability to diagnose canine GM subtypes and grades and discriminate GM from non-neoplastic intra-axial lesions when compared to expert rater opinions derived from qualitative MRI evaluations. | en |
| dc.description.abstractgeneral | Radiomics is a cutting-edge approach that combines advanced computer algorithms with medical imaging techniques like Magnetic resonance imaging (MRI), Computed tomography (CT), Positron emission tomography (PET), ultrasound, and X-rays to uncover patterns invisible to the human eye. By analyzing detailed image data and using artificial intelligence (AI), radiomics provides new ways to diagnose and predict diseases. While this field has been widely studied in human medicine, its use in veterinary medicine is just beginning to be explored. Radiomics could transform how we diagnose gliomas (GM), a type of brain tumor that is particularly hard to identify in medical imaging studies in animals due to cost, logistical issues, and shared features with other diseases. Additionally, conventional MRI techniques often fail to accurately determine GM type and aggressiveness. This research aims to enhance GM diagnosis by using advanced imaging methods, combining both traditional visual and innovative quantitative MRI techniques. We believe that objective, measurable approaches and novel qualitative imaging features will be more effective than relying solely on radiologist' conventional visual assessments. The goal is to develop a more accurate, accessible, and objective tool to assist veterinary clinicians in diagnosing and treating their patients. Chapter 1 reviews the challenges in diagnosing GM with conventional MRI and introduces radiomics as a promising solution, discussing how it integrates AI with quantitative imaging analysis. Chapter 2 demonstrates how tumor size can be effectively assessed to predict response to treatments using simple quantitative measurement methods. Chapter 3 explores the T2-weighted–FLAIR mismatch sign (T2FMM), a key imaging biologic marker in human brain tumors, and evaluates its application in dogs—a pioneering effort in veterinary science. Finally, Chapter 4 outlines a radiomics-based pipeline for analyzing brain tumors, focusing on identifying GM type and aggressiveness, distinguishing tumors from non-tumor conditions, and comparing the performance of AI against expert diagnoses. This work has the potential to revolutionize veterinary brain tumor diagnostics and advance care for both animals and humans. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:42409 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/123918 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Radiomics | en |
| dc.subject | Quantitative Imaging | en |
| dc.subject | Magnetic Resonance Imaging (MRI) | en |
| dc.subject | Glioma | en |
| dc.subject | T2W-FLAIR Mismatch sign (T2FMM) | en |
| dc.subject | Machine Learning (ML) | en |
| dc.subject | Brain tumor segmentation. | en |
| dc.title | Exploring Radiomics and Unveiling Novel Qualitative Imaging Biomarkers for Glioma Diagnosis in Dogs | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Biomedical and Veterinary Sciences | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |