The Applications of Raman Spectroscopy Assisted Urinalysis: Hematuria and Bladder Cancer Detection
| dc.contributor.author | Carswell, William Forrester | en |
| dc.contributor.committeechair | Senger, Ryan S. | en |
| dc.contributor.committeemember | Robertson, John L. | en |
| dc.contributor.committeemember | Zhang, Chenming | en |
| dc.contributor.department | Biological Systems Engineering | en |
| dc.date.accessioned | 2022-04-23T06:00:08Z | en |
| dc.date.available | 2022-04-23T06:00:08Z | en |
| dc.date.issued | 2020-10-29 | en |
| dc.description.abstract | Early detection and screening for urinary tract illnesses is a complex and widespread process which has implications for both preventative care, diagnosis, and treatment monitoring. In this paper, we investigate the use of Raman spectroscopy (RS) for the analysis of urine, a complex biological solution, for the detection of bladder cancer (BCa) and hematuria. Raman spectroscopy is a rapid, low cost, non-destructive analysis method with wide-ranging applicability due to the holistic data capturing nature of the scanning technique. Each Raman scan can be considered a 'snapshot' of the molecular makeup of the sample, and, through proper applications of algorithmic transformation and statistical analysis, many types of assessments can be performed on each sample. In this paper we address creating and utilizing a data pipeline for the purposes of analyzing and characterizing potential samples with hematuria and BCa. The algorithmic transformations utilized include baselining using either the Goldindec or ISREA methods, and intensity normalization. The statistical analysis methods utilized include principal component analysis (PCA), discriminant analysis of principal components (DAPC), analysis of variance (ANOVA), pairwise ANOVA, leave-one-out cross-validation (LOOCV), and partial least squares regression (PLSR). These components of the data pipeline serve to output qualitative or quantitative data, depending on the application. The Rametrix toolbox encompasses the tools required to transform and assess Raman spectra with PCA and DAPC. Using the Rametrix toolbox as well as ANOVA, pairwise ANOVA, and LOOCV, we were able to significantly detect the presence of bladder cancer in a specimen with 80% accuracy. Using the Rametrix toolbox, ANOVA, pairwise ANOVA, LOOCV, and PLSR, we were able to classify samples as pure urine, micro-, or macrohematuria with a greater than 91% accuracy, and quantify the amount of blood in the sample with a high correlation (R-squared value of 0.92). In combination, this style of data pipeline is shown to rapidly and accurately test for multiple symptoms or diseases using similar methodologies. | en |
| dc.description.abstractgeneral | In the United States, over 37 million people live with chronic kidney disease, over 81 thousand new cases of bladder cancer will be diagnosed, and over 17 thousand people will die from bladder cancer. These serious renal and urinary tract illnesses require urinalysis as a major component of detection, diagnosis, and monitoring of the diseases. This level of required testing has significant costs, both in labor and financial impact. Reduction in both the labor and consumable reagent costs associated with urinalysis would serve to improve the ability for the healthcare system provide the necessary testing for these patients, and reduce the risk of shortages in both reagents and staff. We present a new analysis method, termed 'data pipeline', which would take data from a spectrographic data collection method, Raman Spectroscopy (RS), and generate useable output in the form of classification and quantification. These outputs are highly desired for urinalysis, as urine collection is largely the least invasive testing method related to the urinary tract. As we have shown, the RametrixTM toolbox, an algorithmic package of mathematical methods for assessing spectra, is the backbone of a data pipeline capable of detecting both hematuria, an early warning symptom of many urinary tract illness, and bladder cancer, a notably difficult to detect disease, with high accuracy. This method of analysis is non-destructive of the samples, requires no reagents or single use dipsticks, avoids subjective color assessment, and provides rapid results in a repeatable, potentially automatable manner. We investigate a critical component of this process, the baselining method, in order to further examine and refine the methodology by comparing the accuracy and statistical quality of the results with different baseline methods. It is our goal to implement this methodology with the best component processes, in order to achieve a highly robust, accurate tool for assisting in urinalysis testing. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:27901 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/109737 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Urinalysis | en |
| dc.subject | Raman | en |
| dc.subject | Hematuria | en |
| dc.subject | Bladder Cancer | en |
| dc.subject | Chemometrics | en |
| dc.subject | Spectroscopy | en |
| dc.title | The Applications of Raman Spectroscopy Assisted Urinalysis: Hematuria and Bladder Cancer Detection | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Biological Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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