In dogs with large primary tumors, regional lymph node involvement or evidence of distant metastasis can have worse prognoses and significantly decreased survival. Lymph node size alone has been shown to be insufficient as a predictor for the accurate clinical staging of some canine neoplasia, including oral malignant melanoma. However, regional lymph nodes of the oral cavity, such as the medial retropharyngeal lymph nodes, are difficult to access for routine sampling. Diffusion weighted magnetic resonance imaging (DWI) has demonstrated the ability to differentiate metastatic from inflammatory/benign lymph nodes in clinical studies with human cancer patients through the calculation of quantitative values of diffusion termed apparent diffusion coefficients (ADC). The objective of this exploratory study was to evaluate DWI and ADC as potential future methods for detecting malignant lymph nodes in dogs with naturally occurring disease. We hypothesized that DWI would identify significantly different ADC values between benign and metastatic lymph nodes in a group of canine patients with head or neck disease.
Our results demonstrated that two of four observers identified a significant difference between the mean ADC values of the benign and metastatic lymph nodes. When data from all four observers were pooled, the difference between the mean ADC values of the benign and metastatic lymph nodes approached but did not reach significance (P-value: 0.0566). Therefore, our hypothesis was not supported. However, DWI does show promise in its ability to differentiate benign from metastatic lymph nodes, and further studies with increased patient numbers are warranted / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/86612 |
| Date | 14 July 2016 |
| Creators | Stahle, Jessica Anne |
| Contributors | Biomedical and Veterinary Sciences, Larson, Martha M., Dervisis, Nikolaos G., Jones, Jeryl C., Rossmeisl, John H. Jr. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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