Date of Degree
PhD (Doctor of Philosophy)
Jessica C. Sieren
First Committee Member
Joseph M Reinhardt
Second Committee Member
Edwin L Dove
Third Committee Member
Fourth Committee Member
John D Newell Jr
Fifth Committee Member
Eric A Hoffman
Cancer is the second deadliest disease in the United States with an estimated 1.69 million new cases in 2017. Medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), are widely used in clinical medicine to detect, diagnose, plan treatment, and monitor tumors within the body. Advances in imaging research related to cancer assessment have largely relied on consented human patients, often including varied populations and treatments. Tumor bearing mouse models have been highly valued for basic science research, but imaging focused applications are limited by the translational ability of micro imaging systems. Pig models are well suited to bridge the gap between human cohorts and mouse models due to similar anatomy, physiology, life-span, and size between pigs and humans. These models provide the opportunity to advance medical imaging while simultaneously characterizing progressive changes resulting from an intervention, exposure, or genetic modification. We present a foundation for effectively characterizing disease models in pigs, susceptible to tumor development, using longitudinal medical image acquisition and post-processing techniques for quantification of disease.
Longitudinal, whole-body protocols were developed with CT and MRI. Focus was placed on systematic process, including transportation, anesthesia and positioning, imaging, and environmental controls. Demonstration of the methodology was achieved with six pigs (30-85 kg) with four to seven imaging time points acquired per animal. Consistent positioning across time points (CT to CT) and within time points (CT to MRI) was assessed with distance measures obtained from the skeleton following rigid registration between images. Alignment across time points was achieved with an average value of 16.51 (± 12.46) mm observed all acquired measurements. For consistent, retrievable, and complete qualitative assessment of acquired images, structured reports were developed, including assessment of imaging quality and emphasis on tumor development throughout the body. Reports were used to perform a systematic, semi-qualitative comparison of CT and MRI lung assessment with an overall agreement of 72% in detection of disease indicators.
A multi-level registration algorithm was developed to align anatomic structures of interest in the acquired longitudinal datasets. The algorithm consisted of initialization followed by repeated application of a core registration framework as the input data reduced in image field of view. It was applied to align regions of interest in the brain, upper right lung, and right kidney. Validation was performed with overlap (range = [0.0,1.0], complete overlap = 1) and distance measures (range = [0.0, ∞], perfect match = 0.0) of corresponding segmentations with overall results of 0.85 (± 0.11) and 0.41 (± 0.83) mm, respectively. An extension of the algorithm was created, demonstrating the ability to incorporate directional growth and feature extraction measurements into longitudinal tumor progression monitoring. Techniques were applied to a phantom dataset showing solid tumor growth and transition from a non-solid to part-solid lesion in the lungs.
Finally, the developed methods – imaging, structured reporting, registration, and longitudinal feature extraction – were applied to four different porcine models pre-disposed to tumor development. 1) A genetically modified Li-Fraumeni (TP53R167H/+/TP53R167H/R167H) background model showing the development of osteosarcoma and lymphoma. 2) A TP53R167H/+ animal with exposure to crystalline silica showing progression of silicosis in the lungs. 3) TP53R167H/+/TP53R167H/R167H animals with exposure to radiation for targeted sarcoma development and 4) TP53R167H/+ pigs with conditional KRASG12D/+ mutation activated in the lung and pancreas. Whole-body and targeted imaging protocols were developed for each model and qualitatively interpreted by a radiologist using structured reports. Multi-level registration was used to align identified tumors and longitudinal features were extracted to quantitatively track change over time. Overall, the developed methods aided in the effective, non-invasive characterization of these animals.
Cancer is the second deadliest disease in the United States with an estimated 1.69 million new cases in 2017. Medical imaging systems are widely used in clinical medicine to non-invasively identify, diagnosis, plan treatment, and monitor tumors within the body. Advances in imaging research related to cancer assessment have largely relied on consented human patients, often including varied populations and treatments. Tumor bearing mouse models have been highly valued for basic science research, but imaging focused applications are limited by the direct application of developed techniques. Pig models are well suited to bridge the gap between human patients and mouse models due to their biological similarly to humans. These models will allow researchers to methodically cross compare state of the art medical imaging procedure related to the early detection, diagnosis, monitoring, and treatment planning of cancer with direct application to clinical medicine.
In this thesis, we have developed methods for longitudinal tracking of disease development in four tumor prone pig models using current clinical method imaging systems, computed tomography (CT) and magnetic resonance imaging (MRI).
Following image acquisition, a reporting system was constructed for consistent, visual interpretation of images, image alignment was performed on identified tumors, and imaging characteristics were automatically extracted from tumors. Methods were applied to tumor prone pigs with additional exposure to known cancer causing agents. Detected cancers included bone and kidney tumors and lymphoma, and anticipated development of lung and pancreatic tumors.
cancer imaging, Computed tomography, longitudinal imaging, Magnetic resonance imaging, pig models
xvi, 120 pages
Includes bibliographical references (pages 108-120).
Copyright © 2017 Emily Marie Hammond
Hammond, Emily Marie. "Longitudinal medical imaging approaches for characterization of porcine cancer models." PhD (Doctor of Philosophy) thesis, University of Iowa, 2017.